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Control Theory
Multivariable and Nonlinear Methods
Torkel Glad and Lennart Ljung
London and New York
First published 2000 by Taylor & Francis 11 New Fetter Lane, London EC4P 4EE Simultaneously published in the USA and Canada by Taylor & Francis 29 West 35th Street, New York, NY 10001
Taylor & Francis is an imprillt of the Taylor & Francis Group
© 2000 Torkel Glad and Lennart Ljung Translated by the authors from the Swedish Reglerteod: flell Jariabla olinjiira metoder by Torkel Glad and Lennart Ljung. Published by Studenlitteratur 1997.
Dell
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Publisher's Note This book has been prepared from cameraready copy provided by the authors
British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging in Publication Data A catalogue record for this book has been requested ISBN 0748408789 (pbk) ISBN 0748408770 (hbk)
Contents Preface
xi
Notation
xiii
1 Introd uction 1.1 Iv! ultival'iable Systems . . . . . . . . . . . . . . . . . .. 1.2 Nonlinear Systems . . . . . . . . . . . . . . . . . . . . 1.3 Discrete and Continuous Time Models and Controllers 1.4 Some Basic Concepts in Control . . . 1.5 Gain and Signal Size . . . . . . . . . . 1.6 Stability and the Sn1an Gain Theorem 1.7 Comments................
1 2 5 6 8 15 18 22
PART I
25
LINEAR SYSTEMS
2 Representation of Linear Systems 2.1 Impulse Response and Weighting Function .. 2.2 Transfer Function Matrices 2.3 Transfer Operator . . . . 2.4 InputOutput Equations. 2.5 State Space Fornl . . . . 2.6 Discrete Time Systems. 2.7 Comments . . . . . . . .
27 27 28
3 Properties of Linear Systems 3.1 Solving the Systelll Equations . 3.2 Controllability and Observability 3.3 Poles and Zeros . . . . . . . . . . 304 Stability . . . . . . . . . . . . . . 3.5 Frequency Response and Frequency Functions.
43 43
30 30
31
37 41
44 48 53 58
Contents
vi
3.6 3.7 3.8 3A 4
Model Reduction . . . . Discrete Time Systems .. Comments... Proofs . . . . . . . . .
Sampled Data Systems 4.1 Approximating Continuous Time Systems 4.2 System Sampling . . . . . . . . . . 4.3 Poles and Zeros . . . . . . . . . . 4.4 Controllability and Observability 4.5 Frequency Functions 4.6 Comments . . . . . . . . . . . . . .
63
68 77 79 83 83
85 90 93 96 99
5 Disturbance Models 5.1 Disturbances . . . . . . . . . . . . . . 5.2 Signal Size and Scaling. . . . . . . . 5.3 Spectral Description of Disturbances 5.4 Description of Disturbances in the Time Domain 5.5 Estimation of One Signal from Another 5.6 Measurement and System Disturbances 5.7 Observers and Kalman Filters . . . . . . 5.8 Discrete Time Systems . . . . . . . . . . 5.9 Practical Aspects of Signal Sampling .. 5.10 Comments. 5A Proofs . . . . . . . . . . . . . . . . . . .
101 101 103 106 112 114 116 124 131
PART II
145
LINEAR CONTROL THEORY
6 The Closed Loop System 6.1 The Transfer Functions of the Closed Loop System . . . . . . 6.2 Stability of the Closed System. . 6.3 Sensitivity and Robustness . . . . 6.4 Specifications . . . . . . . . . . . . 6.5 Specifications in the Time Domain 6.6 Specifications in the Frequency Domain 6.7 Sampled Data Controllers 6.8 Comments . . . . . . . . . . . . . . . 7 Basic Limitations in Control Design 7.1 Scaling of Variables. . . . . . . . . .
139 140
141
147 147 150 152
155 156 159 164 169
171 . 171
7.2 7.3 7.4 7.5 7.6 7.7 7.8 7.9 7A
Intuitive Analysis . . . . . . . . . Loop Gain Limitations . . . . . . Sand T: Individual Limitations Consequences for the System Performance . Effects of Control Signal Bounds The Multivariable Case Some Examples .. Comments .. .. " .... ., Proofs ........... «
.......
..
....
«
"
. . . . . . . . . . . . .
"
•
B Controller Structures and Control Design 8.1 Main Ideas 8.2 Configuration of Multivariable Controllers . 8.3 Internal Model Control. . . . . . . . . 804 Feedback from Reconstructed States 8.5 Comments . . . . . . . . . . . . . . . ............
.......
172 176 179 186 192 195 201 208 ...... 209 .,
41;
..............
215 216 219 228 233 237
9 Minimization of Quadratic Criteria: LQG 9.1 The Criterion and the Main Ideas . . . 9.2 The Optimal Controller: Main Results 9.3 Some Practical Aspects . . . . . 9.4 Robustness for LQG Controllers 9.5 Discrete Time Systems .. 9.6 Comments. 9A Proofs . . . . . . . . . . .
239 239 242 247 257 264 271 273
10 Loop Shaping 10.1 Direct Methods . 10.2 Formalization of the Requirements 10.3 Optimal1l2 Control . IDA Optimal 1loo Control . . . 10.5 Robust Loop Shaping .. 10.6 Discrete Time Systems .. 10.7 Comments . . . . . . . . IDA To Decrease the Spread of the Singular Values
277
PART III
305
NONLINEAR CONTROL THEORY
11 Describing Nonlinear Systems 11.1 Linear Versus Nonlinear . . .
277 278 284 285 293 300
301 303
307
. 308
Contents
viii
11.2 11.3 11.4 11.5
Examples of Nonlinear Systems . Mathematical Description . . . Equilibria and Linearization . . Comments. . . . . . « . . . . .
312 314 317 319
12 Stability of Nonlinear Systems 12.1 Stability of Equilibria .. . .. 12.2 Stability and Lyapunov Functions 12.3 The Circle Criterion 12.4 Comments . . 12A Proofs . . . . . . . .
321
13 Phase Plane Analysis 13.1 Phase Planes for Linear Systems 13.2 Phase Planes for Nonlinear Systems 13.3 Comments . . . . . . . . . . . . . . .
339
14 Oscillations and Describing Functions 14.1 The Describing Function Method . . . . . . « .. « . 14.2 Computing Amplitude and Frequency of Oscillations 14.3 Comments . . . . . . « . « . 14A Some Describing Functions . . . . . . . . . .
355 356 360 364 366
15 Controller Synthesis for Nonlinear Systems 15.1 Linear Design and Nonlinear Verification. 15.2 Nonlinear Internal Model Control. 15.3 Parametric Optimization . . . . 15.4 Other Approaches . . . . . . . 15.5 State Feedback and Observer 15.6 Comments« . . . . . . .
371
16 Model Predictive Control
383 383 384 385 391
16.1 16.2 16.3 16.4
Basic Idea: Predict the Output kstep Prediction for Linear Systems The Criterion and the Controller Comments. . . . . . . . . . . . . . .
17 To Compensate Exactly for Nonlinearities 17.1 Examples of Exact Linearization 17.2 Relative Degree . . . . . . . 17.3 InputOutput Linearization . . . .
321 323 329 335
337 339 346 352
371 372 374 377 378 382
393 393 395 397
Contents
17.4 Exact State Linearization 17.5 Comments. . . . . . . . .
IX
401 405
18 Optimal Control 18.1 The Goddard Rocket Problem. 18.2 The Maximum Principle . . . . . . . . .. 18.3 Solution of the Goddard Rocket Problem 18.4 Minimum Time Problems 18.5 Optimal Feedback .. . 18.6 Numerical Methods . . . . 18.7 Comments . . . . . . . . . 18A Proof of the Maximum Principle
407 407 410 424 428 436 442
19 Some Final Words
451
444 445
Preface This book is intended for someone with basic knowledge of control theory who wants only one additional book on control in the bookshelf. Control theory is subject to rapid development and many new methods have been introduced in recent years. There are now also many new control design packages that implement advanced control synthesis techniques. One objective with this book is to provide sufficient background and understanding so that such packages call be used in an insightful manner. Another objective is to describe and discuss basic aspects of control design, the approaches, the key quantities to consider and the fundamental limitations inherent in the designs. The book can be used for self study and as a conlplement to program manuals, but is primarily intended as a text book for one or several undergraduate or graduate control courses. It talms off where a typical undergraduate control course ends. This means that we assulne the reader to be familiar with continuous time, singleinputsingleoutput systems, with frequency functions and elementary state space theory. This corresponds to the material in e.g., Franklin, G. F., Powell, J. D. & AmamiNaeini, A. (1994), Feedback Control of Dynamic Syst.ems, 3rd edn, AddisonWesley, Reading, MA. or any equivalent text book. The material can be studied in several different ways. It is, for exalnple, possible to leave out the parallel treatment of discrete tinle systenls, since the basic properties are described in detail for continuous time systems. Part III, on nonlinear systems can be used separately, etc. Please visit the home page of the book
http://www.control.isy.liu.se/controlbook
xii
for ideas on course curricula, and for exercises and software. Many people have helped us in our work. Professor Dan Rivera taught us IMC and MPC and also helped us with the examples on the distillation column. Professor Anders Helmersson taught us Hoomethods and helped us with Example 10.5. Dr. Lars Rundqwist, SAAB AB .. supplied us with the Gripen aircraft dynamics and "approved" our design. Professor PerOlof Gutman shared his knowledge of QFT. Professor Keith Glover has given valuable comments on Chapter 10. Many students in Linkoping have supplied important comments on the material during its development. The book has also been used at the Royal Institute of Technology in Stockholm and Professor Alf Isaksson and Dr. Anders Hansson have shared their experiences in a very useful way. A number of colleagues have commented the manuscript. We want to mention especially Dr. Bo Bernhardsson, Dr. Claes Bretiholz, Professor Bo Egardt, Dr. Svante Gunnarsson, Professor Per Hagander, Dr. Kjell Nordstrom, M.Sc. Per Spangeus and Professor Torsten Soderstroill. Mrs. Ulla Salaneck has helped us with the translation of the book and given us invaluable support with many other things in the preparation of the material. We thank all these people for their help.
Notation Operators, Signals, and Matrices Notation p
q s z l'
e u
y w n V1
Rl,R2,R12
Name
Def in
Differentiation operator Shift operator Laplace variable, replaces p Ztransform variable, replaces q; also the controlled variable Reference signal (setpoint) Control error r  z Control input lVleasured output System disturbance Measurement disturbance Noise source (white noise) affecting the states Noise source (white noise) affecting measured output Intensities for these sources
(2.5) (2.24)
Thm
State space matrices Observer gai 11 S tate feed back
(5.55) (5.64) (8.25)
(6.2) (6.2) (5.55) (5.55)
A,B,C D,N,lv[
I( L
Notation
xiv
Notation
Name
Def. in
Feedback controller Reference prefilter Closed loop system Sensitivity function Complementary sensitivity function also the sampling interval "Covariance matrix" for a signal u Spectrum for a signal 'U %xf(x, t)
(6.1) (6.1)
(6.8) (6.9) (6.10) (5.15c) Sec. 6.2 Chap. 18
Norms We will also work with various vector, matrix, and signal norms. In the table below, z denotes a column vector, A a matrLx, and f a function, e.g., of time (i.e., a signal) or of frequency (Le., a frequency function). A* denotes matrix adjoint, i.e., the matrix obtained by taleing the transpose of the matrix and the complex conjugates of its elements. tr(A) denotes the trace of the square matrix A, Le., the sum of its diagonal elements. Notation
Definition
Name
Izl2 Izj Izlp IAI
Izl~
Euclidian vector norm Weighted vector norm (Operator) matrix norm Largest singular value of A (A(A) are the eigenvalues of A) . 2norm Weighted 2nonll 1l2norm Alternative interpretation Weighted 1l2norm ll oonorm
7J(A)
lAb IAlp 111112
IIfllp 1111100
= z*z
"'12  *P~"'" ",p.:;;,
1
IAzb
SUPz;to 1.::12
(f2(A) = lllax A(A* A) Note that IAI = a(A) IAI~ = tr(A* A) IAI~ = tr(A* PA) IIJII~ = J If(x)l§dx limN4oo ~ If(x)l§dx Ilfll~ = I If(x)l~dx supx If(x)1
I:
Chapter 1
Introd uction Control theory deals with analysis of dynamical systems and methodologies to construct controllers. The object to be controlled can be modeled in several ways. Basic concepts, like feedback, perfonnance, stability, sensitivity, etc., are quite general, but techniques and tools vary with the type of models used. A typical first course in control covers continuous time systems described by linear systems with one input and one output (SingleInputSingleOutput, SISO, systems). In this book we assume the reader to be familiar with this basic theory, including frequency domain analysis and simple statespace representations. The aim of the book is to extend this theory to cover • Multivariable systems (multiple inputs and Inultiple outputs, MIMO) • Discrete tiIne theory • Nonlinear systems For 8180 systems we will also convey a deeper understanding of the fundamental performance limitations that may be at hand. To motivate the ensuing treatnlent, we start by considering two examples, where the behavior cannot be understood from linear SISO theory. Later in this introductory chapter we will treat some general issues in control, like the role of discrete and continuous time models (Section 1.3), list the basic concepts and notations (Section 1.4), and discuss general aspects of stability and signal sizes (Sections 1.5 and 1.6).
Chapter 1
2
1.1
Introduction
Multivariable Systems
A straightforward way to control a multivariable system is to couple each input to an output and then control each pair as a 8180 system. The following examples show the possible pitfalls of such an approach. Example 1.1:
A Multivariable System
Consider a system with two inputs t whose dynamics is given by 1 YI
2
3
1
1 + s+I'U'l
Ul
and
U2,
and two outputs, YI and Y'l,
= s+  l U I + ?U2 s+ ...
Y., =

UI
s+I
(1.1)
Suppose that rl is the setpoint (reference signal) for Yl and r2 is the setpoint for Y2. If we let UI control YI, we could use a PIregulator (1.2) giving the transfer function 21(1
(1.3)
s + 21(1
from rl to YI if 'U2 = O. For 1(1 ::::: 1 we obtain the step response of Figure I.Ia. Similarly, if U2 controls Y2 with the PIregulator (1.4) we have the transfer function 1(2
s
(1.5)
+ 1(2
from T'J, to Y2 if Ul = O. With 1(2 = 2 we obtain the same step response as from Tl to VI, i.e., as in Figure l.Ia. What happens if both controllers (1.2) and (1.4) operate simultaneously? The results are shown in Figure 1.Ib for 1(1 1 and 1(2 2 and a step in TI with T2 O. The control is successful in the sense that YI tends to 1 and V2 tends to zero. The response is however much slower than in case (a). Trying to speed up the response by increasing the gains to 1(1 4 and 1(2 = 8 gives an unstable system, as illustrated in Figure LIe. Notice that this happens despite the fact that each regulator by itself, (1.3) and (1.5), gives stability for all positive gains.
=
=
1 In these introductory examples we assume the reader to be familiar with some standard notation in control tbeory. Formal definitions will be given in Chapter 2..
1.1 Multivariable Systems
3
.,'
(a.) Step response from rl to 1)1 when U2 0 and Ul is controlled from 1)1 by a PIregulator (1.2) with /(1 L The same response is obtained from r2 to 1)2 when Ul ::::: 0 and 1L2 is controlled from 1)2 by a PIregulator (1.'1) with
=
/(2:::::
(b) 1}1 (solid) and 1/2 (dashed) for a stiep in rl when Ul and U2 are controlled simultaneously by PIregulators from 1)1 and 1/2. /(1 1, [(2 2.
2.
']
(c) 1/1 (solid) and 1)2 (dashed) for a step in TI when UI and U2 are controlled simultaneously by PI~regulators from 1)1 and 1)2. /(1 = 4, 1(2 = 8.
Step responses for a multivariable system.
Various control
Chapter 1
4
Of course, nothing forces us to use UI to control YI and could equally well do the reverse and use, for example
U2
Introduction
to control Y2. We
(1.6) This leads to even greater difficulties. Inserting the controllers (1.6) into the system (1.1) gives the following transfer function from Tl to Yl (1.7)
A necessary condition for stability is that all coefficients in the denominator are positive. This means that either ](1 or ](2 must be chosen as negative. This in turn implies that one of the gains must have Uthe wrong sign". The system will then be unstable if it is operated with this controller alone. The result is a vulnerable system where one of the regulators cannot be disconnected. Moreover, it is difficult to tune the controller parameters for good performance. Figure 1.2 shows step responses for 1(1 = 0.75, ](2 = 5.
7'1 (t = 0) and 1'2 (t is controlled from Y2.
Figure 1.2: YI (solid) and Y2 (dashed) for a step in when
'U2
is chosen as PIcontrol from
:lJl
and
Ul
= 2)
~
After discussing the general theory for multivariable systems, we shall • return to this example in Chapter 7. We will show that the fundamental' difficulty is that the multi variable transfer function has a zero in the right half plane (the system is nonmininlum phase), despite the fact that none of the individual 8I80 transfer functions in (1.1) have any' zeros at all.
1.2 Nonlinear Systems
1.2
5
Nonlinear Systems
We know from experience that many physical systems can be described well by linear theory under certain operating conditions, while other conditions may show strong nonlinear effects. An example of this is when a signal reaches a certain physical limit  it is "saturated".
Example 1.2: Current
Currentcontrolled DCmotor with Saturated
A DCmotor with the current as input, u, and angular position as output, y can be described approximately as two integrations ii(t)
= u(t)
(1.8)
The input is always bounded. Assume for simplicity that the limits are ±1
lu(t)l ~ 1
(1.9)
Consider a servo with a linear controller of the form u=F(r
y)
(1.10)
where r is the setpoint for y. Let F be a phase advance (lead) link with transfer function
F(8)
= 30(8 + 1) 8
+ 10
(1.11)
total loop gain for the system will then be 30(8 + 1) + 10)
8 2 (8
(1.12)
crossover frequency 3 radjs and phase margin 55°, which should give reasonable response. When the controller has comput.ed a control outside the bounds (1.9), we simply truncate it to the corresponding This gives step responses according to Figure 1.3. Here, the output been scaled with the setpoint amplitude, so that it tends to 1 in both Figure 1.3a shows the response when the step amplitude is so small the hits its limit during just a short interval. This case therefore shows agreement with what linear theory predicts. The rise time is about 0.3 there is just a small overshoot. When the step amplit.ude is increased the response is as in Figure 1.3b. The rise time here is much longer, as ii;:';:J,t~;ri1lttural consequence of the limit.ation of the input amplitude. In addition, iGr;Y;Uip.'~f~P. that the qualitative behavior of the step response is much worse with H3.l~l!:e ov'er and undershoots. The reason why linear theory fails to describe tlllSDeUClNU)r is clear from the input signal. It is at its bounds practically all and never equal to the value by the controller. ···';;l>lcJ·L.'LJ.
Chapter 1
6
Introduction
1
y ~
',f
y
·V
U
' 0 there is a 6 so that the
figure holds.
Stability of Solutions Stability of a certain solution to a differential or difference equation concerns the question whether small changes in initial conditions give small changes in the solutions. The concepts can be summarized by the following definitions. We state them for the case where the system is given in state space form, i.e.,
x(t)
f(x(t))
(1.31)
where the dot denotes differentiation with respect to time t. (See also The initial values then correspond to the initial state 1.3 Let x* (t) be a solution oj the differential equation (1.31) corresponding to the initial state x*(O). This solution is said to be stable iJ Jor each E > 0 there is a 0 such that Ix*(O)  x(O)1 < is implies thai Ix*(t)  x(t)1 < € for all t > O. (x(t) is the solution that corresponds to ihe initial state x(O).) It is said to asymptotically stable if it is stable and there exists a is such thai  x(O)1 < is implies that Ix*(t)  x(t)1 } 0 as t t 00.
The definition is illustrated in Figure 1.10. Here x*(t) is the nominal and the distance is around x*(O) shows where to start to .."'.......... ,""' in an €environment around x* (t). This definition of stability of solutions to differential equations is also called Lyapunovstability.
22
Chapter 1
Introduction
In Section 3.4 and Chapter 12 we shall return to stability of linear and nonlinear systems, respectively.
1.7
Comments
Main Points of the Chapter In this chapter we have • seen an example of a multivariable system, whose behavior is difficult to understand from SISO theory (Example 1.1) • seen an example of a nonlinear system whose behavior cannot be predicted from linear theory (Exalnple 1.2) • described two ways of handling discrete time controllers (methods I and II in Section 1.3) • presented the general structure for a control system (Figure 1.4) • discussed various types of disturbances (Figure 1.6) • defined signaillorms
IIzll, system gain IIS//,
and stability
• shown the small gain theorem: that a closed loop system is stable if the product of the gains in the loop is less than 1.
Literature Mathematical foundations. Control theory builds on a mathematical foundation and we shall use concepts and results from a variety of mathematical subfields. The prerequisites necessary to fonow this book are contained in normal undergraduate engineering courses. Here we shall give examples of books that can be used to refresh or deepen relevant mathematical knowledge. For Fourier transforms, Bracewell (1978) is a classical and thorough treatment, which goes far beyond what is required here. We also use a fair amount of mat1"ix theory and linem' algebra. For a user oriented account and for numerical applications Golub & Loan (1996) is a standard reference, while Horn & Johnson (1985) gives more theoretical background. A treatment of anailJtical functions and complex analysis is given in Churchill & Brown (1984). For ordinary differential equations we can point to Verhulst (1990). In this book we also treat stochastic disturbance models and
1.7 Comments
23
stochastic processes. A introductory account of this is given in Papoulis (1977). The basic knowledge of control theory that we assume the reader to have, can be found in e.g., any of Friedland (1988), Franklin, Powell & AmamiNaeini (1994), or Dorf & Bishop (1995). Moreover, Ljung & Glad (1994) gives complementary material 011 models of signals and systems. For deeper studies, we point in each chapter to suitable literature. Aluong books that cover broad aspects of the subject, we may mention Maciejowski (1989), Zhou, Doyle & Glover (1996) and Skogestad & Postlethwaite (1996) for multivariable theory and robustness, and Astrolll & Wittellmark (1997) and Franklin, Powell & Workman (1990) for detailed treatments of sampled data control. The mathematical theory of nonlinear systems is presented in Isidori (1995) and Nijmeijer & van der Schaft (1990).
Software To use the tools that we treat in this book almost exclusively requires good, interactive software. There are now many commercially available software packages for control design. Ivlany of these are directly aimed at certain application areas, like the aerospace industry, etc. The most widely spread packages belong to the IvIATLABfaluily with a large number of toolboxes that cover lllost of today's control design methods, including simulation and implmnentation. Another general product is the MATRIxXfamily with design support, simulation and code generation for real time applications. The third fanlily is MATHEMATICA which now has a package for control design, CONTROL PROFESSIONAL. It handles both symbolic and numeric
PART I: LINEAR SYSTEMS This part describes general properties of linear' systems and signals. It has a special focus on mult'ivariable systems. Other than that f the material is of general characterf' corresponding descriptions of signals and systems and their properties can be found in circuit theory, signal processing and basic control theoT]J.
Chapter 2
Representation of Linear Systems In the general classification of systems in Chapter 1, we defined linear 1 time invariant and causal systems. Systems with these three properties constitute, by far, the most common models of control objects. There are two reasons for this: (1) many physical objects can be described well by linear, time invariant models, and (2) such models give good possibilities for analysis and systematic control design. In this chapter we will discuss different ways to represent linear 1 time invariant and causal systems. The multivariable aspects of the syste1TI descriptions will be a special focus. The output at time t, i.e., y(t), is a pdimensional column vector and the input u(t) is an m,dimensional column vector. In this chapter we will not distinguish between control signals and disturbance signals. The vector 1/,( t) can contain signals of both kinds. This does not influence the forms of representation. The representation of continuous time and discrete time systems is quite similar. The main part of this chapter deals with continuous time systems. In Section 2.6 the corresponding expressions for the discrete time case will be presented.
2.1
Ilnpulse Response and Weighting Function
The output from a linear system is the weighted sum of input values at all times. For a causal system only old values of the input signal are included, and for a time invariant system the contributions are weighted by a factor dependent only on the time distance (not absolute tinle).
Chapter 2
28
Representation of Linear Systems
All this means that the output can be written as
y(t)
= 10
00
g(r)u(t  r)dr
(2.1)
The function g( T) is called the weighting function, and shows how much the value of the input T time units ago, now influences the output. It is also the impulse response for the system, i.e., the output that is obtained when the input is an impulse o(t) (Dirac's delta function). The impulse response g( T) is for every Tap x m, matrix. Its (k, j)element shows how output number k (k:th component of the column vector y) depends on the j :th input signal.
2.2
Transfer Function Matrices
The impulse response representation (2.1) gives the output as a convolution between the impulse response and the input. Let us introduce the Laplace transforms
G(s)
= erg) = 10'' ' e·tg(t)dt
U(s)
= £.(11),
Y(s)
(2.2)
= £.(y)
That g(t) is a matrix and y(t) and u(t) are column vectors only means that the integration is carried out elementwise. The Laplace transfonn of a matrix function is thus a matrix obtained by Laplace transforming its elements. Equation (2.1) can now be written as:
Y(s) = G(s)U(s)
(2.3)
provided u is zero up to time O. The product G(s)U(.s) is computed in the normal way as a matrix times a vector, and the resulting elelnents are thus Laplace transfonns of the corresponding output. The matrix G(s) is called the transfer function matrix for the system. It has the dimension p x mi. Its (h~,j)element, Gkj(S), describes how the input j influences the output k. If we only vary this input (and keep the others equal to zero) and only observe the output k, we obtain a singleinputsingleoutput (8180) system with the transfer function Gkj(s). The transfer function matrix is thus a direct extension of the transfer function concept to a multivariable system.
2.2 'Iransfer Function Matrices
29
A transfer function is called proper if the degree of the numerator does not exceed that of the denominator. It is said to be strictly proper if the degree of the denominator is larger than that of the numerator. In the multivariable case we say that a transfer function matrix is (strictly) proper if all its elelnents are (strictly) proper transfer functions. For a strictly proper transfer function matrix G (s) lim G(s)
0
8+00
while the limit is bounded if G is proper.
Example 2.1:
A System With One Output and Two Inputs
Consider the system with the transfer function matrix G(s) = [1/(s
+ 1) 2/(8 + 5)]
Assume that input number 1 is a unit step, that input number 2 is an impulse, and that the system is at rest for t ::; O. We then have
The Laplace transform of the output is then Y(s)
= G(s)U(s) =
1 8(8+1)
2
+ s+5
=
1 1  s s+l
2
+ s+5
and the output is
We will not use explicit time functions to any great extent in this book, transform tables are therefore not particularly important here. However, there might be reason to recall the final value theorem lim y(t) = lim sY(s)
t+oo
8+0
the left hand side exists.
(2.4)
30
Chapter 2
Representation of Linear Systems
Transfer Operator
2. 3
We will use the symbol p for the differentiation operator, d
= dty(t)
py(t)
(2.5)
With the symbol ePT we refer to the operator obtained by formal series expansion of the exponential function: PT_
 1 + pT
e
122 1 kk + "2.P T + ... k!P T + ...
This thus means that
which we (for an infinitely differentiable function 'u) recognize as the Taylor expansion of u(t + T). We can write u(t  T) = ePT'u(t) and inserted into (2.1) we have
y(t)
=
l'"
g(T )u(t  T)dT
=
l'"
g( T )e PT dT u(t)
= G(p)u(t)
In the last step we define the transfer operator G (p) as the integral of g( T )ePT . Note the formal resmnblance to the definition of the transfer function matrix as a Laplace transform of the impulse response: We have only exchanged s for p. The transfer operator has the advantage that we can work directly with the time functions. The link between the Laplace variable s and the differentiation operator is of course also well known from transform theory.
InputOutput Equations
2.4
In the 8I80 case, most often the transfer function is rational in s: G(s)
= B(s) A(s)
where
A(s) B(s)
= sn + alS n  1 + ... + anIS + an = b1s n  1 + b2Sn2 + ... + bnIs + bn
(2.6)
2.5 State Space Form
31
With the transfer operator we have B(p) y(t) = A(p) u(t)
A(P)y(t) = B(p)u(t)
or
which corresponds to the differential equation
dn dtn y(t)
dn 1 + al dt n  1 y(t)
+ ... + anl dty(t) + any(t)
dn I = bi dt n  I u(t)
+ ... + bnu(t)
d
(2.7)
This is a differential equation of order n, only containing input and output. Modeling can often lead directly to such a representation of the system. In the multivariable case the counterpart is a vectordifferential equation (2.7), where the coefficients ai and bi are p x p and p x m matrices, respectively. The counterpart of the rational expression (2.6) becomes G(s) = Al(s)B(s)
or
G(s)
B(s)AI(s)
(2.8)
where A(s) and B(s) are matrix polynomials, defined as above but with the coefficients ai and bi as matrices. The representation (2.8) of multivariable systems is called M atria; Fraction Description, lvIFD.
State Space Form The state of a system at time t represents the amount of information necessary to unambiguously determine future outputs if future inputs are known. If necessary, see Franklin et aL (1994) for a review. It is well known that for continuous time systems, the state space form is obtained, if the relationship between input and output is written as a system of first order differential equations:
x(t) y(t)
= Ax(t) + Bu(t) =: Cx(t) + Du(t)
(2.9)
Here x(t) is the state vector, a column vector of dimension 71., and B, C, and D are matrices of compatible dimensions. A state space representation of a linear system has many advantages, and will be our main alternative in this book. It is an advantage that
32
Chapter 2
Representation of Linear Systems
i TH Tc
Vc
Figure 2.1: A simple heat exchanger. fe and fH are the flows of cold and hot water. Tn, Te, THp Te; denote the temperatures at the marked points, and Ve and VH are the volumes of cold and hot water.
8I80 and multivariable systems formally are treated in exactly the same way. Another advantage is that state space models usually are obtained as a result of physical modeling.
Example 2.2: Heat Exchanger Consider the heat exchanger in Figure 2.1. Its behavior can be described in the following simplified way. The lower part is the "cold part" into which water flows with temperature Te,. The flow is fe (m 3 froin). The upper part is the "hot part" with input water temperature TH; and flow f H. When the flows "meet" (in separate pipes), the hot water heats the cold water to temperature Te. At the same time it is itself cooled to the temperature TH. Let us make the simplifying approximation that the water in the different parts is perfectly mixed, so that it has the same temperatures as the outlet ones, i.e., Til and Te By setting up the heat balance in the cold part we find that the temperature changes according to dTe Ve ::::; fe(To.  To) dt
+ /3(Tn
 Te)
(2.10)
The first term in the right hand side then represents the cooling due to th( inflow of cold water (normally, To; ::; To, so this will give the decrease 0 temperature). The other term corresponds to the heat transfer from the ho' to the cold part of the heat exchanger. It is proportional to the differenci in t,emperature, and the constant of proportionality, /3, depends on the hea transfer coefficient, the heat capacity of the fluids, etc. (In more accurat models the fact that /3 depends on flows and temperatures has to be take: lTltn ~('('()l1nt. but here we fix it to a constant.)
2.5 State Space Form
33
Correspondingly, we have for the hot part dTH VHdt
= lI:f(TH; 
(2.11)
TH)  f3(TH  To)
We now assume that the flows IH and 10 are constant and equal to f and view the inflow temperatures as inputs: Ul = To; and lt2 = T Hi . With the state variables Xl To and X2 = TH we then have the state space representation
[
(1 + (3)/Vo f3/Vf/
f3/Vo
1
(1 + (3)/VH X +
°
[1/Ve
0
1
f /l'H U
(2.12)
y=X We will use the numeric values f VH = Va = 1 (m3 )l which gives .
x
=
[0.21 0.2
Example 2.3:
1 + [0.01 °
0.2 0.21 x
0.01 (m 3 /min),
!3 = 0.2 (m 3 /min) and
0] 0.01 u
(2.13)
The Gripen Aircraft
In a couple of examples we will study the dynamics of the Swedish military aircraft Gripen, manufactured at SAAB AB in Linkoping. It is a good approximation in normal flight to disregard the coupling between pitch angle and turning. In this example we are going to study the latter 1 and consider the aircraft in the horizontal plane. The course of the plane can be influenced in two ways. There is a rudder on the tail fin that can give a direct. turning torque to the plane. There are also ailerons on t.he wings. Their main effect is to let the aircraft roll and thus change its course. We introduce angles according to Figure 2.2. Here are
• ¢ = the course angle (angle between aircraft body and given course typically north) • Vy = velocity across the aircraft body in ydirection • cp roll angle (the rolling angle around the speed vector) • 8a = aileron deflection • flr = rudder deflection
l
We can produce a linear state model for the system that describes the dynamics for small angles rather well. The following state variables are chosen Xl X2 X3 X4
VYl
= P = roll angular rate in a bodyfLxed system,
= r = turning angular rate in a bodyfixed system, = ¢, = ¢, = flul Or X5
X6
X7
Chapter 2
34
Representation of Linear Systems
Figure 2.2: Angles for description of the motion of the aircraft. The xyzaxes mark a fixed body coordinate system. The velocity vector of the aircraft is v. The dashed lines denote north and a horizontal plane, respectively. f3 illustrates the so called side slip angle, the precise definition of which is technically complex. For small angles Vy Rl f3vo, where Va is the velocity.
Note that p and r are the angular rates around xaxis, and zaxis, respectively in Figure 2.2. They differ slightly from ¢ and '¢ since the bodyfixed coordinate system xyz is not fixed in the horizontal plane. As inputs we choose the commanded aileron and rudder angles
which are different from the real rudder angles because of the dynamics in the hydraulic servos. All angles are measured in radians and the time unit is seconds. For the Gripen aircraft the following model applies in a certain case (attack missiles mounted on wing tips, Mach 0.6, altitude 500 m, angle of attack, 0.04 rad) :i;
== Ax+Bu
where 0.292 0.152 0.0364 0 A== 0 0 0
8.13 2.54 0.0678 1 0 0 0
201 0.561 0,481 0.0401 1 0 0
9.77 0.0004 0.0012 0 0 0 0
0 0 0 0 0 0 0
12.5 107 4.67 0 0 20 0
17.1 7.68 7.98 0 0 0 20
2.5 State Space Form
,35
o 2.15 31.7 0.0274 1.48 o
o
B
o
o
o
o
20
o
20
Transformation Between State Space Form and Transfer Operator The state space model (2.9) is in operator form
x =px
Ax+Bu
(2.14)
y=Cx+Du
A)l Bu(t) which leads
This gives (pI  A}x(t) = Bu(t) or x(t) = (PI to
+ Du(t) =
y(t) = Cx(t)
(C(pI  A)l B
+ D)u(t)
= G(p)u(t)
with G(p)
= C(pI  A)l B + D
(2.15)
I
This expression gives the relationship between state representation and the transfer operator (or transfer function matrix: exchange p for s).
Example 2.4: Exchanger
The Transfer Function Matrix for the Heat
From (2.13) we have
G(s)
=I
[8 +0.20.21
0.2 s + 0.21
]1 [0.010 O.~l]
[s + 0.21 0.2 (s + 0.01)(s + 0.41) 0.01
0.2 ] s + 0.21
(2.16)
36
Chapter 2
Representation of Linear Systems
The step from state space form to transfer operator is straightforward matrix algebra. It can be more difficult to move in the other direction. From basic state space theory we know that the transfer function for 8ISO systems,
G(p) =
+ b2pn2 + ... + bn1P + bn pn + alpnl + ... + anIP + an
blpnl
can be represented easily either by controller canonical form
x(t) =
y(t) = [b i
al
a2
anl
1 0
0 1
0 0
an 0 0
0
0
1
0
b2
bn 
1
x(t)
+
1 0 0
u(t)
0
bn ] x(t) (2.17)
or by observer canonical form al
a2
1 0 0 1
x(t) =
b2 x{t)
anl
an
y(i) =
bi
0 0
[1
0 0
+
1 0
0 0 0 0
u(t) bn bn
(2.18)
I
0] x(t)
Using this starting point it is also easy to represent a transfer function matrix with one output and several inputs in observer canonical form. This is illustrated in the following example.
Example 2.5:
System with Several Inputs
Consider the system
We start out by describing the system using a common denominator:
2.6 Discrete Time Systems
37
Assume now that we realize the system from input 1 to the 8180 output in an observer canonical from. Assume then that we realize the 8180 system from input 2 in the same way. The realizations will have the same A and C matrices. The actual output is the sum of the parts originating from the two input signals f and we can simply directly add the effects as follows:
x = [4 5 2
Y=
10 OJ1 x + [14 OJ1
°0
4
1L
1
[1 0 OJ x
The example, with obvious modifications, can also be used to realize a transfer function with one input signal and several output signals in controller canonical form. There is, however, no simple formula for transforming to state form from a transfer function matrix with several inputs as well as several outputs. It is best to introduce states via direct interpretation, either physical or related to the different scalar transfer functions. See Examples 2.3 and 3.1.
2.6
Discrete Time Systems
By definition, discrete time systems only consider inputs and outputs at an enumerable number of tinle points. There are several situations when it is necessary, or natural, to describe a system by a discrete time model. A typical case is that the signal values only are available for measurement or manipulation at certain times, for example, because a continuous time system is sampled at certain times. In Chapter 4 we will treat, in more detail, how discrete thne systelns can be obtained from continuous time systems. Another case is when the system naturally is described directly in discrete form. This is typical for financial systems where, for example, sales figures and inventory levels are naturally associated with a certain day or week. In this section we will consider how to describe discrete time systems. The signals are only defined at discrete time points, and to silnplify matters we enumerate these times using integers. Inputs and outputs are thus defined as
'U.(t),y(t),
t= ... ,2,1,0,1,2, ...
Chapter 2
38
Representation of Linear Systems
Impulse Response A general linear representation from the input sequence to the output at a given time t can be written 00
yet) =
L
gt(e)u(t  £)
(2.19)
£=00
If we limit ourselves to time invariant systems, 9 does not depend on t and for a causal system no influence on yet) from future u can occur. We then have 00
yet) = Lg(f!)u(t  f!)
(2.20)
£=0
The interpretation is completely analogous to the continuous time case. The weighting function g(l!) is here also the (im)pulse response to the system, i.e., the system's response when the input is a pulse:
u(t) =
{Io
0
when t = when t i= 0
Transfer Function Matrices For discrete time signals we use the Ztransform, that is 00
G(z) = Z(g) = L g(t)zt t=o 00
U(z)
=L
00
Y(z)
u(t)zt,
t=O
If values of u, prior to t
(2.21)
= Ly(t)zt t=O
=
0 are zero, the relationship (2.20) can be
written as
Y(z) = G(z)U(z)
(2.22)
Here G(z) is the transfer function matrix for the discrete time system, analogously to the continuous time case (2.3). Everything concerning continuous systems also applies to discrete time systems if the Laplace table is exchanged for the Ztransform table. Recall the final value result: lim yet) 1.;'00
= z+l lim(z  l)Y(z)
if the left hand side exists.
(2.23)
2.6 Discrete Time Systems
39
Transfer Operators The transfer operator is considerably more straightforward in discrete time than in continuous time. We introduce the shift operator q as qu(t) = u(t + 1)
(2.24)
Its inverse, the backwards shift operator, is denoted by ql (2.25) Using the shift operator and the impulse response (2.20) we define the transfer operator as oa
~ g(e)ql
G(q)
(2.26)
£=0
We then have oa
00
y(t) = ~g(e)'lJ,(t I!) = ~g(l!)qf!u(t) = G(q)u(t) £=0
f=O
The transfer operator has, formally, been obtained by exchanging z for q in the definition of the transfer function matrix. Other than that, the same comments apply as for the continuous time case.
Difference Equations A rational transfer operator G(q)
= B(q)/A(q), with
+ b2q n2 + ... + bn qn + alqnl + ... + an
B(q) = b1qnl A(q) =
leads to a difference equation for inputs and outputs as follows:
B(q) y(t) = A(q) u(t)
=}
A(q)y(t) = B(q)u(t)
(2.27)
=}y(t + n) + aly(t + n  1) + ... + anly(t + 1) + any(t) =btu(t + n  1) + b2u(t + n  2) + ... +bnl u(t + 1) + bnu(t) In the above expression we assumed the degree of the numerator polynomial B (q) to be lower than that of the denominator polynomial.
Chapter 2
40
Representation of Linear Systems
In (2.27), this means that we have a time delay between input and output, Le., a change in input at time t does not influence the output until at time t + 1. It is easy to realize that if the degree of the denominator is n as above, while the degree of the numerator is n  k, there is a time delay of k steps between input and output. If the A and B polynomials have the same degree, it means that there is a direct influence from u(t) to y(t). This corresponds to the fact that there is a direct term, i.e., a Dmatrix that is not zero in the state space representation (2.29). Example 2.6:
Inventory Control
Consider a simple inventory model. Let y(t) be the level of the inventory at time (day) t. Let Ul(t) be orders for day t. Ordered goods is, however, not delivered until two days later. Let U2(t) be the sales for day t. The inventory level thus varies as
y(t) = y(t  1) + Ul(t
(2.28)
2)  U2(t  1)
This is a difference equation of the form (2.27), with one output and two inputs, n == 2, and with the coefficients
Here u(t) is a column vector u(t)
1
= [ U2 Ul (t) (t)
We then have y(t)  y(t
1)
= [0
1] u(t  1) + [1
0] u(t  2)
The transfer function matrix for this system is G(z) = [ z2
1
_ Z
1
Z 
1
1
State Space FornI The discrete time variant of the state form is a system of first order difference equations, written as x(t
+ 1) = Ax(t) + Bu(t) y(t) = Cx(t) + Du(t)
with a ndimensional state vector x(t).
(2.29)
41
2.7 Comments
Example 2.7: Inventory Control Let us return to Example 2.6. What is a reasonable state vector for this system? What information do we need for day t in order to know enough about how the inventory situation will evolve? Obviously we need to know the inventory level for the day in question, i.e., yet). Further, we need to know yesterday's orders, i.e., Ul(t 1), since they are still undelivered. We choose x(t) = [
yet) 'Ul(t1)
1
as the state and then have the state space model x(t +
1) = [~
y(t) =
~] x(t) + [~ ~1] u(t)
(2.30)
[1 0] x(t)
In operator form (2.29) becomes
qx = Ax+Bu y = Cx+Du analogously to (2.14). Just exchange p for q. The algebra for the relationship between state space representations and other forms of representation for a linear system is therefore the same for time continuous and discrete time models. All results of the type (2.15), (2.17), (2.18) and Example 2.5 thus apply also for discrete time, by letting q replace p.
2.7
Comments
The Main Points of the Chapter We have shown how linear systems can be represented in different forms. One observation is that the multivariable case in this respect is a straightforward generalization of the 8180 case: it is "just" a question of interpreting certain variables such as matrices and vectors. In addition, the formal similarity between continuous time and discrete time systems is obvious: the differentiation operator p plays the same role as the shift operator q. In the same way, the Laplace variable s corresponds to the Ztransform variable z. With these interpretations aU relationships between the forms of representation look alike in discrete and continuous time.
Chapter 2
42
Representation of Linear Systems
Literature The treatment of continuous tinle systems  linear differential equations  with Laplacetechnique, inputoutput forms and state representation goes back to the pioneers of the 19th century. The discrete time formalism, as described here, was developed in the 1950s and is summarized in classical textbooks such as Jury (1958), Ragazzini & Franklin (1958) and Tsypkin (1958). There are many modern textbooks that treat the material in this chapter. A thorough study of multivariable systems is given in Kailath (1980). Both state space form and matrix fraction description are treated in detail. Descriptions with emphasis on discrete time systems are given in, for example, Astrom & Wittenmark (1997) and Franklin et al. (1990).
Software for Systen1 Representation Analysis and design of control systems is now predominantly done using software packages. Especially, multivariable systems require access to efficient software. We will in the different chapters comment on the character of this computer support. As a starting point we have the CONTROL SYSTEMS TOOLBOX (CSTB), used together with MATLAB. This is probably the ll10st widely spread program package for analysis and control design of linear systems. In MATLAB5 the representation of systems is object oriented and thereby continuous time and discrete time systmlls are treated in parallel in a transparent manner. For the methodology described in this chapter we have the following MATLAB commands. ss, tf Create a system with state form or transfer function form.
Transfornlation between these forms. ss2tf Carry out the transformation (2.15) "statespace to transfer
function" tf2ss Do state realizations for systenls with one input
impulse
Impulse response
step Step response
Chapter 3
Properties of Linear Systems Explicit solutions of the system equations are not widely used in control theory analysis. The focus is, instead, on other concepts describing the properties of the solutions: poles, zeros, frequency function, etc. In this chapter we will discuss these concepts.
Solving the System Equations
3.1
Despite what was said above, it is useful to have the expressions for the general solution of the linear system equations. For system
x = Ax+Bu y
(3.1)
= Cx+Du
it is well known that the solution can be written x(t) = eA(ttO)x(to)
+
i
t
to
For a diagonal Amatrix
eA(lr) B'U(T)dT
(3.2)
44
Chapter 3
Properties of Linear Systems
we have
The ith component of x becomes Xi(t)
= eAi(tto)Xi(tO) +
it
e)..i(tr} BiU(T)dT
(3.3)
to
Here Bi is the ith row of the matrix B. The component Xi thus develops independently of the other state variables. Xi is said to correspond to a mode of the system with the eigenvalue Ai. If we denote the ith column of the matrix C by Ci, the output becomes
(3.4) It is thus a weighted sum of the modes of the system.
In general, if the Amatrix is diagonalizable, we may consider a change of variables in the state space I;. = Tx:
I;.
TAT11;.+TBu
y = CTIl;. + Du
(3.5)
so that T AT I is diagonal. The modes of the system are the components I;.i = Ti X (Ti is the ith row of T) which are certain linear combinations of the original state vector X, corresponding to A '8 eigenvectors. The mode is generally named for its corresponding eigenvalue ("the Inode Ai"). From the general result (3.2) an explicit expression for the impulse response for the system follows:
(3.6)
3.2
Controllability and Observability
Defini tions The concepts of controllability and observability describe how state vectors in the state space are influenced by inputs and how they show
3.2 Controllability and Observability
45
up in the output. They are also important for understanding what happens when factors are cancelled in the transfer operator as well as for the control synthesis with observer and state feedback. Definition 3.1 (Controllability) The state x* is said to be controllable if there is an input that in finite time g'ives the state x* from the initial state x(O) = 0. The system is said to be controllable if all states are controllable. Definition 3.2 (Observability) The state x* lOis said to be unobservable if, when u(t) = 0, t ~ 0 and x(O) = i*, the output is y(t) == 0, t ~ O. The system is said to be observable if it lacks ubobservable states.
Criteria From a basic control course the criteria for controllability and observability are well known, see for exanlple, Franklin et al. (1994), Appendix D. Theorem 3.1 The controllable states of the system (3.1) form a linear subspace, viz, the range of the matrix (the controllability matrix)
(3.7) where n is the order of the system. The system is thus controllable if and only if S has full rank.
A proof of this theorem in the discrete time case is given below (Theorem 3.11). Note that the theorem is valid also if the system has several inputs (u(t) is a vector). If u is scalar, S is a square matrix and has full rank exactly when det S I O. Theorem 3.2 The unobservable states constitute a linear subspace, viz, the null space of the matrix (the observability matrix)
(3.8)
The system is thus observable if and only if CJ has full rank.
46
Chapter 3
Properties of Linear Systems
A proof of this theorem in the discrete titne case is given below (Theorem 3.11). Recall especially that: .. A system in controller canonical form is always controllable • A system in observer canonical form is always observable • A system with one input and one output is both controllable and observable exactly when no cancellations can be made when the transfer operator (2.15) is computed. A state space representation that is both controllable and observable is said to be a minimal realization of the system. There is then no other state representation with lower dimension of the state vector that realizes the same inputoutput relationship.
Controllable and Observable Modes Let the system be given in diagonal form with distinct eigenvalues and consider (3.3) and (3.4). We see that a mode Xi with corresponding row vector Bi = 0 is not influenced at all by the input. If any row in B is zero for a system given in diagonal form, the system is thus not controllable. We then speak of the uncontrollable modes as the state variables Xi, with associated eigenvalue Ai, corresponding to the rows with Bi = O. The other modes are called controllable modes. In the same way, a column Ci = 0 results in a unobservable mode while columns in the Cmatrix that are not identically equal to zero correspond to observable modes. It is easy to verify that the eigenvectors of the controllable modes span the subspace of controllabe states, and similary for the uncontrollable modes. If the system is not in diagonal form, but the Amatrix can be diagonalized, the modes are associated with the eigenvectors of the Amatrix; they are thus linear combinations of the state variables. These modes are controllable, observable, etc., depending on the corresponding row (column) in T B (CTl) in (3.5). If the systenl cannot be diagonalized, the mode concept becomes more complicated.
The PBHTest There is an equivalent test to Theorems 3.1 and 3.2:
3.2 Controllability and 0 bservability
Theorem 3.3 The system (A, B, C, D) in (3.1) is controllable
47
if and
only if [A ),,1 BJ has full rank 'V)..
(3.9)
It is observable if and only if
[
A C AI] has full rank 'V)"
(3.10)
Proof: The proof is simple in the uif"direction: If (3.9) does not have full rank for some ..\ there is a nonzero vector v, such that v [A  AI B] = 0, Le., vA = VA and vB = O. It is easy to verify that vS(A, B) = 0, with S defined by (3.7), so the system is not controllable. To prove the ('only if" direction, the system can be transformed to a triangular block form that will yield the result. See, e.g., Kailath (1980L Section 2.4.3. 0 The criterion is called the PBHtestfor the originators Popov, Belevitch and Hautus. Note that the matrix in (3.9) is n x (n + m), and that it automatically is of full rank for all A that are not eigenvalues of A. The requirement is thus that B "fills out" the null space of A  ),,1 when A is an eigenvalue of A. If this is not the case for some eigenvalue )..i, the corresponding mode is not controllable.
Modifying the Eigenvalues
An important usage of the controllability and observability concepts is to describe when the eigenvalues of the Amatrix can be modified using feedback. If we have the system ± = Ax + Bu and use the state feedback u = Lx the closed loop system will be x = (A  BL)x. In later chapters we win have reasons to study how the eigenvalues of ABL can be modified using the matrix L. Here we give an important formal result on matrices, which shows the connection to controllability.
Theorem 3.4 Let A be an n x n matrix and B an n x 'm m,atr'ix. The m x n matrix L can be selected so that A  BL attains arbitrary, predetermined eigenvalues if and only if (3.11)
has full rank. (Complex eigenvalues must, however, occur in conjugated pairs.)
Chapter 3
48
Properties of Linear Systems
Equivalently formulated: Let C be a p x n matrix. Then the n x p matrix I( can be selected so that A  I( C attains arbitrary, predetermined eigenvalues if and only if
C O(A, C) =
CA 2 CA
(3.12)
has full rank.
The proof is given in Appendix 3A.
Stabilizability and Detectability Theorem 3.4 can be extended to show that if a system is not controllable, the controllable modes (eigenvalues) can be modified, but the uncontrollable cannot. If a certain mode is unstable (the defini tion of stability will be discussed in Section 3.4) it is particularly interesting to know if it is controllable, and thereby can be modified. The following concepts therefore are useful.
Definition 3.3 (Stabilizability, Detectability) A system (A, B, is said to be stabilizable if there exists a matTix L, so that A  BL is stable (i.e., it has all the eigenvalues in the stability region). It is said to be detectable if there exists a matrix I(, so that A  I(C is stable. (The stability region will be defined in Definitions 3.6 and 3.11.) A controllable system is obviously always stabilizable according to Theorem 3.4. Likewise, an observable system is always detectable. The definitions are of great importance in Sections 5.7 and 9.2 on nn1rnY\!l1 state reconstruction and state feedback.
3.3
Poles and Zeros
Poles and zeros are important for characterization of the nT'r\n£.l·rrtO~··x of a system. In this section we will discuss how they are defined (>::1 lrll1.A.tAd for muHivariable systems.
3.3 Poles and Zeros
49
Poles: Definition The eigenvalues of the state matrix A, corresponding to controllable and observable modes describe according to (3.3) the general time response of the system. In particular we have seen in (3.3)(3.6) how the impulse responses are built by ePit where Pi are the eigenvalues of the system. These are thus essential for the behavior of the system. Analogously to the 8180 case we introduce the term poles for these eigenvalues:
Definition 3.4 (Poles) By poles of a system we mean the eigenvalues (counted with multiplicity) of the system matrix A in a minimal state space realization of the system. By the pole polynomial we mean the characteristic polynomial for the Amatrix: det('xI  A). The order of the system is thus equal to the number of poles (counted with multiplicity). '..ICI.,.I.,""l.~"'U.iU.l.V.l.'"
of Poles From the Transfer Function Matrix
realize a multi variable system, G (s ), in minimal state space form may be laborious. Therefore, it would be convenient if the poles could be calculated directly from G(s). In the 8180 case this is simple: The poles are the zeros of the denominator polynomial all cancellations between numerator and denominator). In the case it is somewhat more difficult. We have from (2.15)
G(s) = C(sl  A)l B
+D
 det(si _ A) (C(sI  A)a B
+ D det(sI 
A)).
(3.13)
the second representation we have used Kramer's rule for matri.x Fa is the adjunct matrix to F, obtained by forming det;errninlants of subnlatrices (minors, see below) of F. The point here that the second factor of the right hand side of (3.13) is a matrix with elements that are polynomials in s. From this we realize that all deominator polynomials in G(s) must be divisors to the pole polynOlnial et(sl  A). This consequently has to be "at least" the least common \';"i)i(ienomma,tor for all denominator polynomials in G( s). to be able to calculate poles and to define zeros for system we must use the notion of minors. A minor of a
Chapter 3
50
Properties of Linear Systems
matrix A is the determinant of a square submatrix of A, obtained by deleting rows and columns in A. For example, the matrix
2 3] [1456 has the 9 minors
I! ~ 1=3, I
1 3
4 6
I
=6,
2 3
I5 6
1=3,
1, 2, 3, 4, 5, 6 By a maximal minor we mean a determinant of a submatrix of maximal size, i.e., the 2 x 2 matrices above. We are now ready to state the main result: Theorem 3.5 (Poles) The pole polynomial for a system with transfer function matrix G(s) is the least common denominator of all minors to G(s). The poles of the system are the zeros of the pole polynomial. A proof of this result is given in Kailath (1980), Section 6.5. Example 3.1:
A Third Order System
Consider the system (1.1) 2 5+1
==
G(s)
[
_1
':']
.0;+1
5+1
The minors are 2 s
+ l' 2
(5 + 1)2
3 s+2'
1 and 5+1' 3 5+1 ==(5 + 1)(5 + 2) (5 + l)2(s + 2)
The least common denominator is pes)
== (s + l?(s + 2)
which gives the poles 2 and 1 (double pole), The system is thus of third order and we can now easily find a state representation, e.g" with the states
51
3.3 Poles and Zeros
which gives 1
x=
[~o ~01 2~ ]
. y = [;
x
+
[~0
°Il]
U
(3.14)
~ ~l x
Note that a pole of the multivariable system has to be a pole also of one of the 8180 transfer functions, being the elements of the transfer function matrix. In order to find the poles as such it is then enough to find the least common denominator for the matrix elements. It is to decide the multiplicity of the poles that the minors have to be formed. The total number of poles (counted with multiplicity) is equal to the order of a minimum state representation, which is a useful number to know.
Zeros For a 81S0 system, a zero is a value s that makes the transfer function zero, that is the zeros of G(s) are the poles of l/G(s). Zeros, thus in some sense, describe the inverse system dynamics. For a multivariable system with a square transfer function matrix (as many inputs as outputs) the zeros of G(s) can be defined as the poles of G 1 (s). In order to handle nonsquare transfer function matrices, however, we need a more general definition.
Definition 3.5 (Zeros) For the system (3.1), the (transmission) zeros are defined as those values of s, for which the matrix
M(s) =
[Sl_~A ~]
(3.15 )
does not have full rank. The polynomial that has these values as zeros
is the zero polynomial. For a systmIl with the same number of inputs as outputs, the zero polynomial consequently is det Al (s). The following result, shows how to compute the zeros directly from the transfer function matrix.
Theorem 3.6 The zero polynomial of G(s) is the greatest common divisor for the numerators of the maximal minors of G(s), normalized to have the pole polynomial as denominator. The zeros of the system are the zeros of the zero polynomial.
Chapter 3
52
Properties of Linear Systems
For a proof, see Kailath (1980), Section 6.5. Note that the greatest common divisor also is called the greatest common factor.
Example 3.2:
Zeros
Consider again the system (1.1)
G(s)
=
[st st2] l
5+1
5+1
The maximal minor is
8+ 1
det G(s) = (s
+ 1)2(.9 + 2)
From Example 3.1 we know that the pole polynomial is
pes) = (s
+ 1)2(s + 2)
The determinant is thus already normalized to have the pole polynomial as denominator The zero polynomial thus is s + 1 and the system has a zero in 1. In order to see the relationship with the inverse system we take
G 1 (s)
=[
S!l
S~'2] 1
_1_
_1_
(s+1)(s+2)
5+1
5+1
8+1
=
[(S+~;~:2)

:~~::t
]
2(.9+1)(5+2) s+l
It can now be verified that this inverse system has the pole polynomial s + 1. Thus the relationship (zeros) ¢:> (poles for inverse systems) holds.
Example 3.3:
The Gripen Aircraft
Consider the aircraft model in Example 2.3. Choose the roll angle (X4) and course angle (xs) as outputs, so that
c=[Oo
0 01000] 0 001 0 0
The transfer function matrix is now
G(s) ____1__ [ tll(S)  n(s) t21(S)/S
t12(S) ] t22(S)/S
where
+ 43.38 5 + 543s 4 + 1778s3 + 5203s 2 + 9213s + 177 = (8 + 20)2(S + 2.5876)(8 + 0.0194) x (s + 0.3556  2.9458i)(s + 0.3556 + 2.9458i)
n(s) = s6
and tij (s) are polynomials, which we do not write in detail here. Furthermore
 h2 . t2l detG(s) =      s·n·n
holds. Here tll • t22
t12' t21
46.9s 9 + 4287s 8 + 40896s7
=
 1.036· 108 S5 
2.138.109 8 2


3.603 . lOB S'1
5.67· 106 S6 1.099· 109 s3
4.417 ·108 s  7.7678.10 6
=  46.9(s  77.47)(s  57.4)(s + 0.2165)n(s) so that detG(s) = 46.9 (8 77.47)(8  5;~)(s + 0.2165) s· n s From this follows that. the pole polynomial is s . n( s) and the zero polynomial is 46.9(s  77.47)(s  57.4)(s + 0.2165). The system thus has the poles
o 2.5876 0.0194 0.3556 ± 2.9458i  20 double, corresponding to rudderservo dynamics and the zeros 77.47,
57.4,
0.2165
The system has all poles in the left half plane (however, Gripen is unstable in the pitch channel at low speed) but has zeros far into the right half plane. The poles correspond to modes (see (3.3)) which axe associated with certain motion of the aircraft. The pole in 2.5876 is the 7'Oll mode, which describes the rolling of the aircraft. This has a time constant of approximately 0.4 sec in Gripen. The pole in 0.0194 is the spiral mode which mainly describes the response to the turning angle. The complex conjugated polepair corresponds to the so called Dutchroll mode which is a combined turning and rolling motion. The pole in the origin is the integration to the course angle from the underlying angular velocities.
3.4
Stability
Stability is a key concept in control. In Section 1.6 we have already treated inputoutput stability and stability of solutions in the state space. Here we will relate these definitions to linear, time invariant systems. For this, we first need a technical result on norms for powers of matrices.
Chapter 3
54
Properties of Linear Systems
Powers of Matrices The following results show how the eigenvalues determine the size of matrix powers and matrix exponentials: Theorem 3.7 If the matrix A has the eigenvalues .AI, ... ,.An (possibly multiple) the matrix F = eAt has the eigenvalues
Proof: If A is diagonalizable, the proof is trivial. If A cannot be diagonalized, a matrix R exists so that
A
= RUR 1
where U is upper triangular with the eigenvalues along the diagonal (with zeros below). Then Uk is also upper triangular and has along the diagonal. From this we have that
).r
is upper triangular with eAit along the diagonal. These values are then also equal to the eigenvalues of the matrix. Then
and therefore
eAt
and
eUt
have the same eigenvalues, which concludes the
~~
0
Theorem 3.8 Lei .Ai, i = I, ... , n, be the eigenvalues of the matrix F. Assume thai
Then, for some positive constants C and Ci we have (3.16) and (3.17) Here
I . I is
the matr'ix norm {operator norm} (1. 24)·
3.4 Stability
55
The proof is given in Appendix 3A.
Corollary 3.1 Lei Ai, i A. Assume that He Ai
.The Discrete Time Nyquist Criterion (For a function, analytical except in a finite number of poles, the number ~of zeros and poles can be decided using the argument principle: If the /function J(z) has N zeros and P poles in an area r then N  P = number of times the curve J(z(8») encircles the origin when z(8) follows 'the boundary of r in positive direction (counter clockwise) Using this result, we can compute the number of zeros inside and :outside the unit circle for an arbitrary polynomial. The method is perhaps most useful when studying the stability of ~a closed loop system obtained by feedback with 1. Let the system
y(t) = Go(q)u(t)
pe
(3.45)
a given, stable system. We assume as usual that the degree of 'the numerator in Go(q) is lower than the denominator. The system is
Chapter 3
74
Properties of Linear Systems
closed by the feedback 1:
u(t)
r(t)  y(t).
The closed loop system thus is
Go(q)
(3.46)
y(t) = 1 + Go(q) r(t)
This system is stable if it lacks poles outside the unit circle. Since Go is assulned to be stable, the question is if the function
R{z)
= 1 + Go{z)
has zeros outside the unit circle. The function R( z) is rational with the the same degree in numerator and denominator and all poles poles of Go) are inside the unit circle. We want to test that also all zeros lay inside the unit circle, i.e., if N  P = O. This is according to the argument principle the same as showing that the curve R{e iO ) does not encircle the origin when B goes from 0 to 27T, since z(8) = eiO then encloses the unit circle. Alternatively we can test if the curve Go{eiO ) encircles the point 1. This gives the following discrete time version of the Nyquist criterion: If the system Go (q) has no poles outside the unit circle, the closed loop system l~~~q(~) is stable if and only if, the curve Go{e iO ), 0 S 8 1.
Example 3.8: System
< 27T does not encircle the point
The Nyquist Curve for a Discrete Time:
Consider the system G(q)
I(
q 0.5
We have
G(e iO )
= I«(cosB 
0.5 isine) 1.25  cos ()
This function is plotted for 0 S () < 27r in Figure 3.4. We see that the point: 1 is not encircled when I( < 3/2. The closed loop system (3.46) is therefore, stable for these values of I(.
Discrete Time Systems
75
3
2.5 2· 1.5
" > ~', "
/
/
III
~eLI
/ I
0.5
.§
( ·2K/3
"
\\ \ 2K I
0
0.5 1 .1.5
2
2
I
o
2
3
Reill Axis
3.4: The Nyquist curve for the system in Example 3.8. The right crossing point with the real axis corresponds to B = 0, while the left one corres'pOIlds to B = 1T.
An interesting aspect of the Nyquist criterion is, that, as for the continuous thne system, stability margins in terms of "how close the curves are to encircle" 1 can be defined. See Figure 3.5. The argument of the Nyquist curve, when it enters the unit circle (or rather its deviation from 180°) is such a measure. This angle, marked in Figure 3.5, is called the phase margin of the system. The interpretation is the same as for the continuous time systems. If Go(e iw ) is plotted as a Bode plot (Le., log IGo(eiw)1 and argGo(e iw ) is plotted against logw) the phase margin is thus 1f
+ arg Go(eiw")
where w* (the crossover frequency ) is defined by IGo (e iw * ) I
1.
The Frequency Function Consider a discrete time stable transfer function matrix (3.47)
Properties of Linear Systems
Chapter 3
76 2
1.5
... 
/
/ /
0.5
"
  ...
"
,, \ \
I
\
I
I
o
\ I
~t\
I
:
y~y , \
0.5
\
.
.
'..
1

I
/
/ ."./"  _ .... #'
•
.].5 2
2
1.5
]
0.5
0
0.5
1.5
2
Figure 3.5: The phase margin is the angle
tp.
Now let input number k be a discrete time cosine function:
e= 0, ±1, ±2, ... Let 00
Gjk(Z) =
L 9jk(m)Zm m=O
The output j is then 00
Yj(f) =
L 9jk(m)uk(e 
00
m) =
m=O
= Re {
L 9jk(m)Re
eiw(lm)
m=O
(~gjk(m)eiwm )
eiWI }
= Re{Gjdeiw)eiWI}
= /Gjk(e iw )/ cos(ew + 1. We are going to show "Heyman's Lemma":
If SeA, B) has full rank there is an m x 11. matrix F for every column vector b = Bv =/:. 0 such that also S(A + BF, b) has full rank.
According to what we just shown we can always select a row vector f! such that A + BF  be assumes arbitrary eigenvalues. By using L = F + vi the theorem will be proved. Now it only remains to show Heynman's Lemma. We first show that we can always find a series ofvectorsvk,k = 1, ... ,nlsuch that Xl = bjXk+l = AXk + B'Uk gives a sequence of vectors .'Ck which spans the entire space. If this were not the case, it would mean that for some k < 11.
where £. is the space spanned by Xl"",Xk' This means that AXk E £. (take = 0) and therefore BVk E £, 'rIvk. In turn this would mean that AXj = Xj+l  BVj E £;j = 1, ... ,k 1, thus x E £, => Ax E £. The conclusion would be 'rIw, Bw,ABw,li2Bw, ... ,AnIBw all would lie in [, with rank < n. This contradicts the fact that S(A, B) has full rank. Consequently, we can find a sequence Vk which gives a sequence Xk which spans the entire space. Now define F by FX/~ = Vk. (This can be done since the xkvectors span the entire space.) We can write Xk = (A + BF)k1b, which now proves that S(A + BF, b) has full rank. The "if"part of the proof is thus completed. To show the "only if"part, we use the PBHtest (3.10): If the system is not observable there is a value .\ *, such that the matrix in (3.10) does not have full rank. That is, there is a vector x such that
'Uk
[
AA*1] C :&=0
i.e.,
(A  .\ * 1)x
=0
and
Cx
=0
This means that (A  I(C)x = A*x for all values of IC The eigenvalue.\* can thus not be changed, which proves the "only if" part of the proof. 0
Appendix 3A: Proofs
81
Proof of Theorem 3.8 Proof: First assume that F is diagonalizable. Then there is a matrix L such that
.
F=L
[P
~
o ] L loch
Ft
=L
[
p~ 0
Pn
From this (3.16) and (3.17) follow directly. If F cannot be diagonalized, there is anyway a matrix L such that F = LJL 1 with
J=
PI
el
0
0 0
P2 0
e2
0 0
P3
fa
0
0
0
0
0 0 0 enI
Pn
where ei = 1 or O. ei can also be 1 only if Pi = PH1 (This is on the condition that multiple eigenvalues are sorted together in J.) The form J is called Jordan normal form. (For a diagonalizable matrix ei = 0 for all i). By mUitlplllcl:t.tl(m we see that Jt is a triangular matrix with zeros below the main diagonal. In the diagonal there are elements of the type p~ and in the super Cllal:;onal there are elements of the type p(t)pi where p(t) is a polynomial in t degree not higher than ni 1 where ni is the multiplicity of the eigenvalue Since
now follows that all elements in the matrix Ft are bounded by CAt. From follows that also the norm of this matrix must satisfy the same bound perhaps with another C), so (3.16) follows. Also (3.17) follows from the that a matrix norm is at least as large as the largest eigenvalue of the
o
Chapter 4
Sampled Data Systems By a sampled data system we mean a discrete time system that is obtained by sampling a continuous one. It can be seen as an approximation, or as an alternative description of the system, where we are only interested in the values of the input and the output at a number of discrete time instances, often uniformly distributed in time u( t),
t = eT;
e = . .. ,2, 1, 0, 1, 2, ...
Here T is the sampling interval and t eT are the sampling instants. The frequency Ws = 27f/T is called sampling frequency, while WN = ws /2 = 11" IT is the Nyquist frequency. In this chapter we will discuss the relationships between a continuous time system, and the discrete time system describing the relationship between input and output at the sampling instants t fT.
4.1
Approximating Continuous Time Systems
Today almost all control objects, that "physically" are continuous time systems, are controlled by sampled data (discrete time) controllers. We will discuss this further in Section 6.7. As we noted in Section L3, there are two main choices for obtaining such regulators ("Method I" and "Method II"). There are thus two typical situations for describing a continuous time system by means of a discrete time one:
84
Chapter 4
Sampled Data Systems
I. We have calculated a controller as a continuous time system and want to implement it in a computer as a discrete thlle control algorithm.
II. We have a continuous time description of a control object, and want to use discrete time design theory to decide a sampled data controller. For this we need a sampled (discrete time) description of the control object. The first situation is the question of digital implementation of controUers and is covered in detail in the literature on digital control. In short, the main idea is to replace the differentiation operator p in the continuous description by a suitable difference approximation, i.e., an expression in q. Euler's classic approximation corresponds to replacing p by (q  l)/T i.e., .() x t
~
(x(t +T)  x(t)) T
where T is the sampling interval. A better and often used approximation is the so called bilinear transformation, also called the trapezoidal rule or Tustin's formula
(4.1) In most cases, the resulting discrete time controller will give a closed loop system with properties close to what would be obtained using the original continuous time controller. One condition is that the sampling is "fast", typically about 10 times the bandwidth of the closed loop system. In this chapter we will concentrate on the other situation, to describe a continuous control object using a discrete time system. If the input to the continuous time system can be reconstructed exactly from its values at the sampling times (for example if it is piecewise constant or piecewise linear between the sampling instances) it should be possible to obtain a discrete time representation exactly describing the sampled output.
4.2 System Sampling
4.2
85
System Sampling
To Go From a Continuous System to its Sampled Counterpart Consider a continuous time system in state space form ±(t)
y(t)
= Ax(t) + Bu(t)
(4.2a)
Cx(t)
(4.2b)
We assume that the system is controlled by a sampled data controller with the sampling interval T. See also Section 6.7. The input u(t) is then piecewise constant: u(t)
u(kT),
kT ~ t
< (k + l)T
(4.3)
According to (3.2) the solution of (4.2) can be written
x(t)
= eA(tto)x(to) +
l.t
eA(ts)B u(s)ds
(4.4)
t·o
If kT ~ t
< (k + l)T we chose in (4.4) to = kT and get
rt eA(ts) B u(kT)ds
x(t) = eA(tkT)x(kT) +
ikT
= eA(tkT)x(kT)
In particular, at t = (k
+
(1:
e A (t8)dS) B u(kT)
(4.5)
+ l)T we have
x((k + l)T) = eAT x(kT)
+
(£
(k+l)T
) eA(kT+Ts}ds
Bu(kT)
.kT
= Fx(kT)
+ Gu(kT)
(4.6)
with the matrices (4.7a)
(4.7b)
86
Chapter 4
Sampled Data Systems
The last equality holds if A is invertible. Since y(kT) = C x(kT)
(4.8)
(4.6) and (4.8) give a description of the the sampled system. We have shown the following:
Theorem 4.1 (Sampling of systems) If the system
x=
Ax+Bu y=Cx
(4.9a) (4.9b)
is controlled by an input, piecewise constant over the sampling interval T, then the relationship among the values of the input, the state and the output at the sampling instants, is given by the sampled data system
x(kT + T) = Fx(kT)
+ GU(kT)
(4.10a) (4.10b)
y(kT) = H x(kT) Here
H=C
(4.10c)
The sampled system (4.10) thus only gives the output and state at the sampling instants. Their values in between these instants can be calculated using the general expression (4.5). In the state space representation (4.10) there is always a delay because u(kT) influences y(kT + T) and not earlier values of y. This is natural considering the way the sampling normally is done: first the output y(t) is read at t = kT, after that the input u(kT) is chosen. There is no logical possibility for u(kT) to influence y(kT). On the other hand, y(t) is normally influenced for t > kT~ even if this is noticed first at the next sampling instance t = kT + T. To find the sampled counterpart to a continuous system, it is only necessary to calculate the matrix eAt. There are several ways to do this. Here we only recall (4.11)
4.2 System Sampling
87
where [,1 is the inverse Laplace transformation. To compute F and G the following algorithm could be used:
T2 T3 Tk+l As 2 e ds=IT+A+A  , + ... +Ak(k o 2 3. + 1)r+'" . F=I+AS G=SB. S=
I
T
In the calculation of S the series expansion is truncated when the last term is smaller than a given E. There are, however, several considerably better algorithms. See the literature references. Example 4.1:
The Double Integrator
Consider the system 1
y=u
p2
With the state variables Xl
= y,
the system can be written in state space form:
[!~ ]
=
y
A=
[~ ~] [ ~: ] + [ ~ ] u
(4.12)
= [1 0] [ ~: 1
[~ ~],
B= [
n,
c = [1 0] 1
1
s
gives
n
[~ G= [[~ ~] [~]dt=[T;{2l F=
]
88
Sampled Data Systems
Chapter 4
The counterpart to (4.12), sampled by T is then given by
= [ 0IT1 1x(kT) + [ T2/2] T u(kT)
xCkT + T)
(4.13)
= [1 0] x(kT).
y(kT)
The transfer operator is GT(q)
(qI [~ I ]r' [Tf2]
= [1
0]
= ~T2
(q + 1) q2  2q + 1
2
Example 4.2:
(4.14)
A Direct Current Motor
Consider a DC motor 1 with transfer operator given by
1 Y = pep + 1) u With the state variables representation
[
;~ ] = [~ y

=
Xl
y,
=
X2
i 1[ ~~ 1+ [ ~ ]
iJ
we have the state space
u
(4.15)
= [1 0] [ ~~ 1
This system is sampled using the sampling interval T. We then have eAt
= £1
=
[
1
S
o
S
+1 ]
[1o 1eet
t
1 = [£1 S1 £1 8(8+1) 1 £1
0
1
£1_1_ s+l
]
and
G=
re
10
T
At B dt
=[
.r:j~T
e t) dt ] etdt
(1 
= [ (T  1 + eT T) (1  e )
]
The sampled data system is given by x(T + kT)
=[
y(kT) =
1 1  e0 eT
T
]
x(kT)
+
[
(T  1 + e(1 _ e T )
T
)
1u(kT)
[1 0] x(kT) (4.16)
4.2 System Sampling
89
The transfer operator is
1
(4.17)
To Go from a Sampled Data System to its Continuous Time Counterpart We assume that a discrete time systeln is given
x(t + T)
Fx(t)
+ Gu(t)
(4.18)
We are looking for a continuous time system, that will give (4.18) when it is sampled with the sampling interval T. In principle, it is simple to find. We first solve the equation (4.19) with respect to A, and then determine B as
(4.20) This is done most easily if F is in diagonal form. com plications:
There are two
• (4.19) may have no solution • (4.19) may have several solutions. We illustrate this in two examples. Example 4.3:
A System without Continuous Counterpart
The scalar discrete time system x(t + 1) = x(t) y(t) = x(t)
+ u(t)
does not have a continuous counterpart of order 1, since eO (real) solution 0:.
= 1
lacks a
90
Chapter 4
Example 4.4:
Sampled Data Systems
Harmonic Oscillator
Consider the sampled system (the harmonic oscillator)
x(t + T)
= [ co~ aT
 sm aT
sin aT cos aT
1x(t) + [ 1 .smcosaTaT 1u(t).
It is easy to verify th at
satisfies (4.19), (4.20) for this system if
f3
= a + T27r . k,
k
= 0, ±l, ...
There are thus infinitely many continuous systems that correspond to our + . k)i. sampled system. They have the eigenvalues
Generally speaking, complex conjugated poles in the continuous time system, cannot be calculated unambiguously from descriptions of the sampled system. Normally, when calculating the continuous time system, it is chosen to have complex poles with the smallest possible imaginary part. In the example above we then have the solution f3 = a if and only if
laTI < 1L
(4.21)
This relationship gives the upper bound for the sampling interval to make reconstruction of the continuous time model possible. This is in agreement with the sampling theorem that states that we have to sample with a sampling interval T < 1r/wt in order to be able to describe the frequency w t correctly in a signal. The relationship between the different continuous time and sampled representations for a system can now be summarized by the scheme in Figure 4.1.
4.3
Poles and Zeros
Poles The poles of the continuous system are, if the system is controllable and observable, equal to the eigenvalues of the A matrix. When the system
4.3 Poles and Zeros
91
Sampled
Continuous G(p)
x(t) = Ax(t) + Bu(t) y(t) = Cx(t)
x{t + 1) Fx(t) + Gu{t) y(t) = Hx(t)
Figure 4.1: Relationship between different system models.
is sampled we form F = eAT. Its eigenvalues are easily calculated from the A matrix according to Theorem 3.7. If also the sampled system is controllable and observable (see next section) the eigenvalues of the F matrix are equal to the poles of the sampled system. Under this condition we have shown The poles of the sampled system are given by . e>.·T 1 , Z = 1, ... ,n where Ai, i = 1, ... ,n, are the poles of the continuous system.
Let us illustrate this relationship. We have ;\ = /l
+ iw
=}
e>.T
= e pT (cos{wT) + i
sin(wT)).
(4.22)
From this expression we see first that when the sampling interval T is small, the poles of the sampled system will concentrate around 1. We also see that if the continuous system is asymptotically stable, < 01 the sampled system will also be stable; le>.TI = el1T < 1. The "+'£"r>c. (4.22) can also be illustrated as in Figure 4.2. Note that 4.2c also can be interpreted as if>. is kept constant and T grows o to infinity.
It
is considerably more difficult to describe the relationship between the zeros of the continuous time system and the discrete time system. in particular
Chapter 4
Sampled Data Systems
•
(a)
.... ,
....
, \ \
\ \ ~~4~~~
I
\ \
I \
... ,
I
"
.....

/
_
",,/ ....
(b)
\ \
\
...
_
" , .....
....
I
\ I
\ \
...
I
', .....
....
""
,, /
(d
\ \ \
...
" ' ....

Figure 4.2: Relationship between the poles of the continuous and the sampled system.
4.4 Controllability and Observability
93
., The sampled system can have both more and less (see Example 4.1) zeros than the continuous time system . .. The sampled system can be nonminimum phase even if the continuous is minimum phase and vice versa.
4.4
Controllability and Observability
Controllability means, for sampled as well as continuous systems, that the state zero can be controlled to an arbitrary state in finite time. When controlling the sampled system, we only have access to a subset of the input signals that can be used for the continuous system, i.e., piecewise constant signals. In order for a sampled system to be controllable the underlying continuous system has to be controllable. We also realize that if the choice of sampling interval is bad, it might happen that the sampled system cannot be controlled using the input signals that are available, even though the continuous system is controllable. Observability means that only the state zero can produce an output sequence that is identically zero when the input is zero. For a 0, while for the continuous time system it is necessary that y(t) sampled system y(kT) = 0, k = 0, I, .... It can happen that the output is zero at the sampling instants but not identically zero for all t. The sampled system can therefore be unobservable even if the continuous system is observable. With a badly selected sampling interval both controllability and observability can be lost. H a continuous time system is unobservable there are concealed modes that do not show up in the output. For a sampled system unobservable modes do not have to be particularly concealed. They do not show in the output at the sampling instants, but in between they affect the output, if the underlying continuous system is observable. Unobservable modes can thus be highly visible during sampled data ...,\..1 ..."" .....,.... It is important to keep this in mind since observability can be by feedback. We give an example of this . .L:.JA.U.L.L.&ple 4.5:
The Double Integrator
Uo:nSltr:1er control of the double integrator
G(p) =
1
Chapter 4
94
Sampled Data Systems
Figure 4.3: Output and input when (4.25) controls the double integrator ret) = 1. with a sampled data controller with sampling interval T = 1. The control algorithm sees the sampled system 1
yet)  2y(t  1) + yet  2) = 2(u(t  1)
+ u(t 
2))
(4.23)
according to calculations in Example 4.1 (see equation (4.14)). If the output should follow a reference signal ret), it appears from (4.23) very suitable to select u(t  1) such that 1
'2(u(t  1) + u(t  2)) = 2y(t  1)
+ y(t 
2)
+ r(t)
(4.24)
since (4.24) inserted into (4.23) gives
yet) :::: ret), i.e., the perfect servo properties. The controller (4.24) can be written
u(t) Of,
= 4y(t) + 2y(t 
1)  u(t  1) + 2r(t + 1)
(4.25)
with operator notation
tt(t)::::
4 + 2ql 1 +q 1 y(t)
2
+ 1 +q 1 ret + 1)
(4.26)
Figure 4.3 shows how the control system behaves when the sampled controller (4.25) is applied to the double integrator for 1·(t) = 1. From this figure we see two things:
4.4 Controllability and Observability
95
L The calculations have been correct: the output is identically equal to the reference signal at the sampling instants.
2. The controller is worthless if the control aim is to ma1ce the output follow the reference signal for an t, not only for t = 1,2, .... The result implies that the feedback (4.26) bas created unobservable modes. Let us analyze this. Inserting (4.26) into the system
we have
Le., y(t)
=
1 + ql :;.r(t) {I + ql)(1  2ql + q2  ql (2 + ql)) 1 +ql 1 + ql r{t) r{t)
=
In the closed loop system we can thus cancel the factor 1 + ql. We know that this means that the closed loop system has a mode with the eigenvalue 1, that is uncontrollable and/or unobservable. The situation will be completely clarified by analyzing the feedback in state space form.
The double integrator can according to Example 4.1 be represented in state space form as x(t
+ 1) =
1 x(t) + [ 1/2] 1 u(t) [10 1]
y(t) =
[1 0] x{t)
where x(t)
y{t) ]_ [ Xl (t) ] [ lj(t) x2(i)
The first row in the state equation is
y(t + 1) = y(t) + X2(t)
1
+ 2"u(t)
(4.27)
96
Chapter 4
Sampled Data Systems
i.e.,
X2(t)
= yet + 1) 
1
yet)  "2 u (t).
From the second row we get
X2(t + 1) = X2(t}
+ u(t)
= yet + 1)  yet)
1
+ 2"u(t).
The feedback law (4.25) can now be written
u(t)
1 = 2y(t)  2(y(t)  yet  1) + "2u(t 1)) + 2r(t + 1) =2Xl(t)  2X2(t) + 2r(t + 1) =  [2 2] x(t) + 2r(t + 1).
Inserted into (4.27) we get
! 1x{t)
x{t + 1) =
[~
x{t+1)=
[~2 ~1
~] x{t) + [ ~ ] ret + 1)
[;
Le.,
]X{t)+ [; ]r{t+l)=FX{t)+GT{t+1) (4.28a)
yet} =
[1 0] x(t)
= Hx(t).
(4.28b)
For (4.28) we have
The system is thus unobservable. The unobservable subspace is spanned by x*
= [ ~ ], which according to
(4.28) corresponds to a mode with the
eigenvalue 1. This explains exactly our problem with the cont.roller in (4.25).
4.5
Frequency Functions
Let the continuous system (4.9) have a transfer function matrix G(s) = C(s1  A)l B
+D
and let its sampled counterpart (4.10) have the transfer function matrix GT(Z) = H(z1  F)le + D
4.5 Frequency Functions
97
Let the system be driven by the piecewise constant cosine signal u( eT) cos(wTI!)j e = 1,2 .... The calculations following (3.47) show that the output at the sampling instants will be given by
The frequency function for the sampled system is thus GT(e iwT ).
Note the factor T! It serves as normalization of the frequency: w has the dimension "radians/time unit" while T has the dimension "time/sampling intervaL" The combination wT they always appear together then has the dimension "radians/sampling interval" which can be understood as a normalized frequency. Note that the piecewise constant cosine signal we have used is not a pure cosine. The lower the frequency w the more similar will the original cosine signal be to its piecewise constant counterpart. This should mean that G(iw) and GT(e iwT ) will be approximately equal for small wT. We have the following relationship:
Lemma 4.1
(4.29) where g(T) is the impulse response of G(s).
Proof: Let G(iw)
= LX> g(r)eiTWdr
We have 00
 eikwT G T (eiwT)  """ L..tgk k=l
where
9k
is the impulse response of the sampled system. From
y(fT) =
f"
g(r)u(fT
r)dr
f: lkT
k=l
g(r)dr u(eT  kT)
(kl)T
which holds if (4.3) is true, we realize that
9k
l
kT
(kl)T
g(r)dr
(4.30)
Chapter 4
98
Sampled Data Systems
Then
IG(iw)GT(eiwT)1 ==
:s; 2: 00
k=1
f
[kT k=1 {kl)T
max
{kl)T$:r$kT
:s; wT·
f
[kT
k=1
(kl)T
(g(r)e iTW _ g(r)eikWT)dr
le iWT  e ikwT \. Ig(r)jdr == wT
[kT Ig(r)ldr {kl)T
roo Ig(r)ldr
10
applies, which gives (4.29).
o
The two frequency functions then agree at low frequencies. The rule of thumb is that up to one tenth of the sampling frequency (w < 2n/(lOT)) the difference is small. For a system that is sampled fast compared to frequencies of interest, the sampled frequency function can be considered to give a good picture also of G (iw). Example 4.6:
The Sampled Frequency Function
Consider a continuous time system with the transfer function
1 G (s) == :+s+~1
(4.31)
Its frequency function is shown in Figure (4.31). If we write (4.31) in state space form and sample according to Theorem 4.1 for some different sampling intervals T and then calculate the discrete time transfer function we get, T
= O.ls:
T
= 0.5s:
T = Is:
T =28:
0.0048ql + 0.0047q2 GT(q) = 1  1.8953ql + 0.9048q2 0.1044ql + 0.0883q2 GT(q) = 1  1.4138q1 + 0.6065q2 0.3403ql + 0.2417q2 GT(q) = 1  0.7859ql + 0.3679q2 G _ 0.8494q1 + 0.40409q2 T(q)  1 + 0.1181ql + 0.1353q2
The frequency functions GT(e iwT ) for these discrete time systems are also shown in Figure 4.4. We see, as expected, that the shorter the sampling interval the better the correspondence between the sampled and the continuous time system.
Arv:.wru D
1111
liP
)0' 10'
10'
lIIo (rad/tiek)
1(11
W!
rrekven.~
101
Itl"
to l
freben.; {rad/sek)
Figure 4.4:
The continuous system's Bode diagram together with the sampled system. The curves are from top to bottom: the cont.inuous system, T = O.ls, T = 0.5s, TIs, T = 2s. Note that they end at the respective Nyquist frequencies.
4.6
Comments
The Main Points of the Chapter
A continuous time system can be approxhnated by a discrete time system in several ways. If the input of the continuous time systern is constant over the sampling periods, the output at the sampling instances can be described exactly with a discrete time system by the sampling formu]a (4.10). The relationship between a continuous time system and its sampled counterpart can be symbolized by
where T is the sampling interval. This basic relationship is ShOWll~ for example, in the coupling between poles (A t eAT) and the argument of the frequency function (iw + eiwT ).
Literature The material in this chapter is covered in detail in standard textbooks on sanlpled data controllers, for example Astrom & Witten mark (1997),
100
Chapter 4
Sampled Data Systems
Franklin et al. (1990). The relationship between the continuous and the sampled system's zeros is treated in Astrom, Hagander & Sternby (1984). A good overview of numerical methods to calculate eAT is given in Chapter 11 in Golub & Loan (1996). Sampling also has several practical aspects regarding alias, prefiltering, etc. More thorough descriptions can be found in standard textbooks on digital signal processing, for example Oppenheim & Schafer (1989), as well as in the previously mentioned books on sampled data control.
Software The transformations between continuous time and discrete time systems are obtained in MATLAB by the commands c2d, d2c. In MATLAB 5 representation and properties of discrete time and continuous time systems are covered in parallel.
Chapter 5
Disturbance Models 5.1
Disturbances
We have mentioned earlier that the control objective is to make the system's output behave in a desirable way despite the influences of different disturbances. To achieve this, it is necessary to understand what disturbances are, and to describe their character in a suitable way. These types of questions will be discussed in this chapter. We have described the relationship between control signal and output in transfer operator form as y(t} = G(p)u{t)
(5.1)
In practice we are never so fortunate that the llleasured signal exactly follows {5.1}. It will deviate from G{p)u(t) in different ways. These deviations depend on disturbances, and the real output will be written y(t}
G(p)u(t)
+ w{t)
(5.2)
where w{ t) is called the disturbance signal. From a system perspective, w is of course nothing but yet another input, to the system it is the fact that it is not at our disposal to control the plant that leads to a distinction. What is the origin of disturbances? In principle, the reason is that there are many factors in addition to our control input that influence the output. The sources of such influences, could be for example: • Load variations: This is often the primary disturbance and the reason for control. It is a matter of signals beyond our control
Chapter 5
102
Disturbance Models
that influence the system, for example, wind gusts disturb aircraft control, the number of persons in a room directly influence the temperature, the load of an engine influences the speed, etc. In process industry a common disturbance of this character is variations in raw material. The measurement of the output is influenced by noise, drifts in the measurement instruments, signal transmitting errors, inhomogeneity in the material being measured, etc.
o Measurement errors:
• Variations in plant and actuators: Wear and changes in the plant as well as imperfections in the actuators, create disturbance sources. • Simplifications and model errors: An important reason for the output not following (5.1) exactly, is of course that the model (5.1) contains errors and simplifications.
Realizing that the real output as in (5.2) can differ from that of the model is a way to malm the controller less sensitive to model error. There is, however, reason to emphasize that such "disturbances" w are of different character than the external ones: they will depend on the input u, which means that it may be more difficult to predict their size and properties. What does a typical disturbance look like? Let us look at some signals from actual control systems. Figure 5.la shows temperature variations in a room, and Figure 5.1 b the moisture content variations in manufactured paper in a paper machine. How should the disturbance be described? In order to determine a suitable controller for the system (5.2) we need to describe the properties of the disturbance wet) in a suitable way. The classical approach is to use standardized disturbance types such as impulses, steps, ramps and cosine signals. In this chapter we will use a more general approach and discuss three aspects: flo
To describe the "size" of the signal. (Section 5.2)
• To describe the frequency content of the signal. (Section 5.3) • To describe the signal as an output from a linear system driven 1~," "nrl.;+a TIr.ic:tp" or an "imoulse". (Section 5.4)
103
5.2 Signal Size and Scaling
(a) Room temperature. Between time t = 10 and t = 15 the window has been opened. Between time t 100 and t = 160 a number of persons are in the room.
(b) Moisture content in manufactured paper.
Figure 5.1: Output signals in some control systems. No control applied. After that, in Section 5.6 we discuss how the disturbances can be incorporated in the system descriptions. How relevant information of the system can be estimated and reconstructed from the disturbed measured signals will be examined in Section 5.7.
5.2
Signal Size and Scaling
In Section 1.5 we defined the 2norm for a signal z(·) as (5.3) Here z(t) is a column vector and Iz(t)12 = zT(t)z(t).
Scaling The size measure (5.3) has weaknesses and it can be misleading if the different components are of completely different size. Assume that we have, for example,
104
Chapter 5
Disturbance Models
with Zl = 100 and Z2 = 1, thus Izi = 100.005. If Z2 is changed to 2, that is by 100%, Izi is changed to 100.01 which is only 0.005%. The problelTI can be avoided by scaling the variables. If zf represents the variables measured in the original physical units) scaled variables can be formed using Zi
=
z{ / di,
i
= 1, ... , n
(5.4)
where di is selected as the maximUlTI of the absolute value of the component z{. This guarantees that IZil S; 1 for all i. We can also wri te the scaling as Z
= D 1 z1
(5.5)
where D is a diagonal matrix with di as ith diagonal element. To make the scaling effective, it is conlmon to first deduct a mean value from z1 before the scaling (5.4). The problem with weighting the components in z can also be handled using weighted quadratic norms
Iz(t)l} = zT(t)pz(t)
(5.6)
where P is a symmetric positive semidefinite matrix. IIzIIp then means the 2norm according to (5.3) where Iz(t)l~ is used instead. The relationship between scalar variables according to (5.5) and weighted nornlS can obviously be written
Ilzl/ = I/z 1 1ID2
(5.7)
A Matrix Measure of Signal Size For a vector signal z(t) with m, rows we can define the m x m, matrix (5.8)
as a variant of (5.3). We use the notation Rz to associate to the concept the "covariance matrix for z(t)", which is a good intuitive picture of (5.8). We will return to this interpretation in Section 5.3. If Rz is known for a signal z, we can calculate the sizes of a number of related signals. If we, for example, form a scalar signal using a linear conlbination of the components of z, s(t) = Lz(t), we obtain
Ilsll~ =
.r
Lz(t)zT(t)LT dt
=L
.r
z(t)zT(t)dtLT
= LRzLT
(5.9)
105
5.2 Signal Size and Scaling
Example 5.1:
Signal Size
Let the signal u(t) == [Ul(t)
R u 
U2(t)]T be of size
[153 3]1
The signal z(t) == 3Ul(t)  U2(t) than has the size
This means that the size of the kth component of z is given by the kth diagonal element in R z • Ilzll~ will then correspond to the sum of the diagonal elements in the matrix R z . The sum of the diagonal elements of a square matrix A is called the trace of A and is denoted tr( A). For the trace we have the useful identity
tr(AB) = tr(BA)
(5.10)
The conclusion is that the usual norm (5.3) is related to the nlatrix measure Rz as
Ilzll~ = tr{R z )
(5.11)
The different cOlnponents can of course also be weighted as in (5.6) in the scalar measure. It is then easy to see that we have the general relation
Ilzll~ = tr(PR z )
(5.12)
Power Measures A drawback for all measures of signal size we have used so far, is that many signals do not have finite energy. This means that the integrals in (5.3) and corresponding expressions are not convergent. In such cases it is instead natural to consider the "mean power" in the signal, i.e., the limit (for simplicity we write it "onesided" from 0 to 00)
IIzll; = I)
1 hnl N •
N+oo
iN Iz(t)1 0
2
dt
(5.13)
106
Chapter 5
Disturbance Models
and correspondingly for the other measures. A drawback of (5.13) is that it has certain formal, mathematical limitations. The limit in question does not have to exist. Also /I . lie can be zero without the signal being zero. The measure is therefore not a formal norm. These situations are, however, exceptions. Normally 1I·lIe exists for all signals of interest. In the future, everything we say about the 2norm also applies to 1\ . lie.
5.3
Spectral Description of Disturbances
Spectral Properties To describe the properties of a disturbance it is useful to consider its spectrum or spectral density. (The correct mathematical term is spectral density. In this book we will, however, follow engineering praxis and use the term "spectrum".) The spectrum of a signal, in principle, indicates how its energy (or power) is distributed over different frequencies. The spectral concept is used and defined in several different contexts, and is, basically, the square of the absolute value of the Fourier transform of the signal (perhaps normed, averaged, etc.). Generally, this means that in the scalar case we assume the signal u to be formed by cosine signals according to u(t) =
i:
A(w) cos(wt + (w) )dw
(5.14)
The (energy)spectrum of u, denoted by illu(w), is then (the expected value of) A 2 (w). In this book we are not going to study definitions of spectra in depth, but refer to text books on signal theory. We only need three· properties: 1. An m,dimensional signal u(t) can be associated with a Hermitian· (see Definition 3.9) m x m,matrix function ~u(w), called its: spectrum.
2. Let G be a linear, stable system. If u has spectrum cpu(w) and y(t)
= G(p)u(t)
(5.15a) .
then y has spectrum cpy(w)
= G(iw) 1 then becomes larger than the one corresponding to 181 < 1. The faster an unstable pole is (the greater Re Pi is), the worse the sensitivity becomes. Note that the loop gain is unstable not only if the original system G is, but
182
Chapter 7
Basic Limitations in Control Design
6
1
8
9
10
Figure 7.5: lliustration of Bode's integral theorem. The two areas are equaL
also if the controller Fy is unstable. There is thus good reason for avoiding unstable controllers. We also get a confirmation of our original conclusion of Section 7.2, that unstable systems are difficult to control. 5. The "repayment" in the form of sensitivity greater than 1 cannot take place at arbitrarily high frequencies. This can be seen by estimating the tail of the integral. If WI is sufficiently large, we have the estilnate
1
00
WI
log
\1
1
00
1
+ GFyl
~
dw S
1
WI
00
Wl
log(l
log
1 dw 1 IGF],I
+ IGFy/)dw ~
1
00
IGF;,ldw (7.23)
WI
Since GFi, has to decrease at least as w 2 for the theorem to be valid, there is a constant C such that (7.24)
for large enough w. The tail of the integral can then be estimated
7.4 Sand T: Individual Limitations
with
1
00
< log IS(iw)ldw ~
Wl
1
00
WI
183
Cw 2 dw = C
(7.25)
WI
The contribution to the integral from frequencies where GF;1 is small (i.e., from frequencies well above the bandwidth) is thus very small.
Limitations of Specifications In the previous chapter we discussed specifications of the sensitivity that have the form
IIVVsSlloo :::; 1
(7.26)
There are several limitations on the choice of liVs, to avoid specifications that are impossible to satisfy. Bode's integral gives some limitations. Other limitations arise from the fact that
S(z)
= (1 + G(z)Fy(z))1 = 1
if z is a zero of the loop gain. This relation is often referred to as an interpolation condition.
Theorem 7.4 Let Ws be stable and minimum phase, and let S be the sensitivity function of a stable closed loop system. Then (7.26) can not be satisfied unless
IWs(z)1 :::; 1
(7.27)
for every right half plane zero z of the loop gain GFll'
Proof: The proof is based on the fact that llVsS is analytic in the right half plane and the fact that an analytic funct.ion achieves its maximum absolute value on the boundary of a set. Let the last fact. be applied to a set enclosed by the imaginary axis and a half circle which is expanded to enclose all of the right half plane. Since the limit of 111'sSI on the half circle equals t.he limit when w + ±oo, we get sup IWs(iw)S(iw)1 ~ I\JVs(s)S(s) I w
for all s in the right half plane. If s is chosen as a zero of the loop gain, then 0 (7.27) holds, since S becomes 1. Below we will use this fact to get a bandwidth limitation for nOllminimum phase systems.
184
Chapter 7
Basic Limitations in Control Design
The COlnplementary Sensitivity Function The complementary sensitivity fUllction has to satisfy a number of conditions, similar to the ones restricting the sensitivity. The following condition is an analogy of Bode's integral theorem. Theorem 7.5 Let the loop gain IG(s)Fy(s)1 have a higher degree in the denominator than in the numerator and assume that G(O)Fy (0) =1= O. Let G(s)Fy(s) have 1\;1 right half plane zeros {not counting the imaginary axis, but counted with multiplicity} Zi, i = 1, 2, ... , M. Then G(s}Fv(s} T() s = l+G(s)FlJ(s} sa t'zsfi es tf,te re l at'zan
roo IT(iW) I dw io log T(O) w2
=
7r el ~ 1 2" T(O) + 1r ~ Re Zi
(7.28)
where el is the error coefficient, cf {6.33}: el = lim dT(s) 8400
ds
= lim dS(s) 8400
ds
Proof: The result is shown by computing
f (
T(S)) ds
log T(O)
S2
along an integration path, which in the limit encloses the right half plane, in analogy with the proofs of Theorems 7.3 and 7.2. (For the case of a loop gain with at least two poJes at the origin, the proof can be directly obt,ained from the proof of Bocle's integral theorem. This is done by using the relation
which shows that T(Sl) can be regarded as the sensitivity function of a loop gain FlI (Sl )1 G(sl) 1 which has a numerator degree which is at least two units higher than that of the numerator. The result then follows from the fact that the poles of Fy(Sl )lG(S1 )1 are the inverses of the zeros of Fy(s)G(s).) 0
We make some remarks. 1. For the case Fr = Fy and T(O) = 1, the error coefficient el gives the stationary error when the reference signal is a unit ramp.
7.4 Sand T: Individual Limitations
185
2. If the loop gain has at least two poles at the origin, then T(O) = 1 and el = O. We then obtain an invariant integral for ITI in analogy with the one for sensitivity of Theorem 7.3. If it desired to decrease ITI at some frequencies then it has to increase somewhere else. 3. If the loop gain has precisely one pole at the origin then we still have T(O) = 1, but el is nonzero. The integral of log ITI depends on el (which normally is positive). It might therefore be easier to get a decrease in IT\ if a greater value of el is accepted. 4. The restrictions on T of the two cases above become more severe if there are zeros in the right half plane, especially if they are close to the imaginary a.xis. 5. If the loop gain contains no pole at the origin, then T(O) i= 1. In that case the ratio IT(iw)jT(O)1 satisfies an invariance condition. Equation (7.28) is then consistent with ITI being small at all frequencies. There is, however, still the fact that S is close to 1 when T is small which has to be taken into account.
Limitations when Specifying T In the previous chapter we discussed robustness specifications of t.he form
IIH'TTlloo ~ 1
(7.29)
If the loop gain has a pole at PI, then
T(Pl)
=1
This gives the following limitation in the choice of lifT. Theorem 7.6 Let {VT be stable and minimum phase. Then (7.29) can not be satisfied unless IVVT(Pl)1 ~ 1
(7.30)
for every right half plane pole PI of the loop gain GF;,. Proof: In analogy wit.h Theorem 7.4 we get sup IliVT(iw)T(iw) I 2?: IWT(S)T(s)1 w
for all s in the right half plane. If s is chosen as a right half plane pole, the result follows. 0
186
7.5
Chapter 7
Basic Limitations in Control Design
Consequences for the System Performance
Consequences for Unstable Systems We shall now use the results of the previous sections to get a deeper understanding of the difficulties with unstable systems. The intuitive reasoning of Section 7.2 suggested that a bandwidth larger than the unstable pole is required. We will first look at this question using Bode's integral theorem. Suppose we control an unstable system G having a real pole at s = PI, with a controller F y • Let M be the maximum value of lSI. We then have from (7.21) 1TPl
=
P1
1 o
log IS(iw)ldw +
100 log jS(iw)ldw Pl
~ PI log !vI +
roo log IS(iw)jdw
Jp1
Suppose the loop gain for angular frequencies above PI is negligible. We can use the same reasoning as with (7.25) to see that the contribution from the last integral is negligible and get log M
~ 1T
=> M
~ err: ~
23
This is normally quite unacceptable. It follows that the loop gain above PI cannot be too small. This agrees qualitatively with our intuitive conclusion that the bandwidth has to be greater than Pl. A more precise quantitative estimate can be derived from Theorem 7.6. Suppose we require
IjlVTTI\oo ~ 1,
i.e., IT(iw)1 ~ IWil(iw)1
with WT chosen as s
1 To
VVT=+WO
The Bode diagram of Wi l is shown in Figure 7.6. We thus specify a bound on the peale of ITI parameterized by To, and an upper bound on the bandwidth, parameterized by woo If the loop gain has a real pole at PI, we get from Theorem 7.6 PI
IWT (p I ) I ~ 1 => Wo
1
+ To ~
1 =?
w
>
0 
PI 1  liTo
(7.31)
7.5 Consequences for the System Performance
1+~
187
W
Figure 7.6: Bode diagram of VVil.
If we accept To = 2, we have to choose Wo > 2Pb a ruleofthumb which is often given.
Example 7.1:
Balancing
We want to balance a stick on the finger, see Figure 7.7. The input is the acceleration of the finger and the output is the angle of the stick with respect. to a vertical line. The length of the stick is e and its mass is m. We consider (
Figure 7.7: Balancing. The lower end point of the stick has the coordinates
eand (= o.
horizontal motion by the finger along a line. The position is given by the coordinate~. The input is then u =~.
Chapter 7
188
Basic Limitations in Control Design
The center of mass has the horizontal coordinate
(; e. '" + 2 smy and the vertical coordinate
e
cosy 2 We view the point of contact between the finger and the stick as a hinge without friction. The force F from the finger then must point in the direction of the stick. The Newtonian force equation gives
F cosy  mg I
:=
d22 (J!ry cosy ) := mrye(y... msm y dt
...
....
.'l
y~
cosy )
for the (component and . 11 F S1n
:=
'')') m",c+ md22 (e. ? smy ) := mu + me(..y cosy  ysmy dt ... 2
for the ';component. Eliminating F and simplifying we obtain
'2e..Y 
.
gsmy:= ucosy
(7.32)
For small angles we get the transfer function 2/C
G(s):= '}
s
The poles are
2(1
r
±jii and the system is unstable. From our argument above
we should have a bandwidth well in excess of )2g/[, say 27r)2g/1! rad/s. The stabilization is thus more difficult to achieve for a short stick than for a long one. The practical Jimitation comes from the bandwidth of the handeye coordination. There are variations from one individual to another, but 10 Hz (time to react ~ 0.1 second) is a good figure. This corresponds to a stick that is about 20 cm long. For a pole of one meter, a bandwidth of about J25 ~ 4.5 Hz is This can be U~U.,UU.I"''''''''''
Consequences for Time Delays A systelll with time delay can be written in the form
Let us once more consider Equation (7.9) frolll Section 7.2: u
= G 1 (G cr
 (1  S)w)
7.5 Consequences for the System Performance
189
It is now impossible to achieve ideal control G c + 1, S + 0, since it would give the noncausal controller
w)
u=Gll(s)eSTd(r
To calculate u(t), one would have to know r(t + Td) and w(t + Td). Thus a realistic controller can only be obtained if G c and 1  S = T contain a delay e sTd which is cancelled when multiplying with Gl. The best approximation of an ideal control then becomes
Gc
= e STd ,
T
e sTd
The corresponding ideal sensitivity is S
=1
e STd
(7.33)
In Figure 7.8 we have given the ideal sensitivity function of (7.33) for Td = 1. For small values of w we get S(iw) ~ iwTd from (7.33) .
/ ".,/ .. •.•.
10'
w
Figure 7.8: Ideal sensitivity function for a unit time delay. The ideal sensitivity function thus has a magnitude less than 1 up to approximately w = l/Td. This is also approximately the bandwidth, WB, of S, so we have (7.34) for a system containing a tilne delay.
190
Chapter 7
Basic Limitations in Control Design
Consequences for NonMinimum Phase Systelns In Section 7.2 we discussed nonminimum phase systems and said on intuitive grounds that there would probably be an upper liInit to the bandwidth. We will now use Theorem 7.4 to get a quantitative value. Suppose we require 8 to satisfy
IIWsSl/oo
s 1,
i.e., 18(iw)\ S IWil(iw)1
Vw
where Wi! is given by
WS 1
_

SoS s + woSo
with the Bode diagram of Figure 7.9.
We require S to suppress
1+~~~~~~~~~
w
Figure 7.9: Bode diagram of Wi!.
disturbances in the frequency interval up to Wo and we also set an upper bound on its magnitude, parameterized by So. If the loop gain contains a real zero z > 0, then Theorem 7.4 gives (7.35)
If we accept So = 2 then we have to take Wo S z/2. Even if we accept So ~ 00 we have to choose Wo :::; z. The bandwidth of a system with nonminimum phase loop gain can realistically not be greater than approximately half the value of the zero. Another point of view is the following. Assume that the loop gain has a real zero z > 0, which is the only right half plane zero. We can write the loop gain in the fonn
zsG(s)F(s) = G(s)F(s) z+s
7.5 Consequences for the System Performance
191
where G(s) is a minilnum phase systenl. For s = iw we get
IG(s)F(s)1
=
IG(s)F(s)1
rhe gain crossover We is the same for GF as for the minimum phase system GF. The phase margin is, however, decreased by the factor (z  iwc)/(z + 'iwc). For We = Z this gives a decrease of phase margin which is 7T /2. The phase shift of GF can then be at most IT /2 to get any stability at all. From Bode's relation this is the phase shift given by having a slope of 20dB/decade, which is quite typical at the crossover frequency. To get a reasonable stability margin we have to take a gain crossover frequency well below w = z. It is interesting to compare the bandwidth limitations of nonminimum phase and time delay systems. Approximating the time delay with a rational function gives
~
1  sT/2 1 +sT/2
with a zero in 2/T. From (7.35) with So = 2 we get Wo < l/T, which is the sanle limitation as we got when reasoning directly about the time delay. Finally, we will also look at nonminimuln phase system limitations in the time domain. Consider a system G(s) with a right half plane zero:
G(z)
Rez > 0
0,
Choose an arbitrary controller Fr(s) = Fy(s) = F(s) giving a stable closed loop system with static gain equal to 1. The Laplace transform of the closed loop step response is
yes)
(I + G(s)F(S))lG(s)F(s)~ s
Hence yes) = 0 for the zero s Laplace transform
o=
Y{z)
=
= z.
(Xl y(t)eztdt ./0
We get, using the definition of the
(7.36)
(Here we have used the fact that the Laplace integral is convergent for s > 0.) Since the static gain is 1, yet) eventually tends to one, and in
192
Chapter 7
Basic Limitations in Control Design
particular gets positive. For the integral to be zero, y(t) then also has to take negative values. We can estimate the magnitude of the negative values. Let Ts be the settling time to within 10% of the final value, i.e.,
Iy(t)  11 :::; 0.1
for t
> Ts
and define the undershoot IvI
1
y(t)eztdt =
1
=  mint2:o y(t).
From (7.36) we get
y(t)eztdt
y>O
y~O
where we have divided the time axis into intervals where y has constant sign. The interval where y :::; 0 must be contained in [0, TsJ and the intervals where y > 0 must contain at least [Ts, 00]. We then get the following estimates
and conclude that
AI > 0.9 e:::Ts
 zT.'i
(7.37)
For small values of zTs the undershoot will thus be very large. For a given value of z, there is therefore a lower limit on the settling time Ts if a reasonable step response is wanted. This agrees well with the bandwidth limitations we discussed above. Conversely, if z is much greater than the inverse of the desired settling time, then (7.37) will cause no problems.
7.6
Effects of Control Signal Bounds
Consider a control problem defined by (7.38) where d is a disturbance. Assume that the variables are scaled as described in Section 7.1. We then have
/d(t)1 :::; 1,
lu(t)l:::; 1
7.6 Effects of Control Signal Bounds
Let us focus on disturbance suppression and put the control signal
193 l'
= O. We see that
(7.39) gives perfect disturbance rejection. The question is whether this control signal can be applied considering the bound ju(t)1 ::;; L Theorem 7.7 A necessary condition for all disturbances with Id(t)1 ::;; 1 to be perfectly rejected by control signals satisfying lu(t) I ::;; 1 is thai
IG(iw)1 ~ IGd(iw)l,
all w
(7.40)
Proof: If d is a pure sinusoid, then from (7.39) the steady state u becomes a sinusoid amplified by the factor IG(iw)lGdUW)I. This factor then has to be less than 1, which gives the result. 0
The condition given by (7.40) is obviously also sufficient if we consider steady state rejection of a single sinusoid. For more general disturbances it is not sufficient, as shown by the following example.
Example 7.2:
Disturbance rejection
Let G and Gd be 1
G(s) = 2s + l'
Gd
1.4 (s + 1)(8 + 2)
In Figure 7.10 their amplitude curves are shown. We see that the requirement of Theorem 7.7 is satisfied, but with no margin. The control signal which gives perfect disturbance rejection is
u=
2.8s + 1.4 d (s+1)(s+2)
If d( t) is chosen as
d(t) =
I { 1
OStD
I
u
0
lui ~ D
1
u 1
u
lui
1
u 1 C~
1
Example 14.6: Dead Zone
A dead zone is described by the graph
feu)
Its describing function is
;~
(arcsin(D/C)
+ !f;Jl 
(g)2)
c~D C
115
75
115
L~~~~====,=o==t~~====~t&==~,a=:J~
O\~~~~~~7~~~~~~to·
K
(b) The criterion for b = 1.1, as a function of IC
(c) Step response using the optimal controller (b = 1.1, J( = 8). Solid curve: y, dashed curve: u.
Figure 15.4: Criterion and step response using parametric optimization of lead compensator for DC motor with saturation.
15.4 Other Approaches
377
If the system is linear and L is a quadratic function, then the criterion V will consist of second moments of various signals, depending on 1J, i.e., integrals of the form
In =
~ [o:J
1Bn(iw, 1J) 12 dw 211" Jo:J An (iw, 1J)
with
A n (s,1J) = sn + an_ls n 1 + ... + ao B n (s,1J) bn_1s n  1 + ... + bo
(15.1)
where the coefficients may depend on 1J in an arbitrary manner. These integrals can be efficiently calculated using a state space representation of B / A and Theorem 5.3. If the covariance matrix of the state II::; is known, then the norm of Cx equals CTI::;C T according to (5.9). In MATLAB the computation can be done as follows: We first make a state space realization: » num = [bni bn2 ... bl bO] j » den = [1 ani an2 ... al aO]; » [A,B,C] = tf2ss(num.den); We then solve (5.62) using PI = lyap(A,B*B'); Finally we get the integral by J = C*PI*C';
»
The discrete time case is completely analogous. The only difference is that lyap is replaced by dlyap.
15.4
Other Approaches
Model Predictive Control A natural framework for controller design is the predictive power of a model of the controlled object. Using such a model, the influence of controller action on the interesting variables can be predicted. The control signal can be chosen so that a given criterion is minimized, subject to physically motivated bounds on the signals. There are several methods based on this philosophYl such as MPC (model predictive control), GPC (generalized predictive control) and DMC (dynamic matrix control). We will present them in Chapter 16.
Exact Linearization A useful method of handling nonlinearities is exact linearization. The basic idea is to use an inner loop which exactly compensates the
378
Chapter 15
Controller Synthesis for Nonlinear Systems
nonlinearities. In the outer loop, the system then becomes linear and a linear design is possible. The difficulty of compensating the nonlinearity varies strongly with the application. In some cases it is trivial, in other cases extensive calculations might be necessary to decide if it is even possible to compensate exactly. We will discuss methods of exact linearization in Chapter 17.
Optimal Control In Chapter 9 we saw controllers for linear systems designed by optimization of a quadratic criterion. For nonlinear systems it is natural to allow more general criteria, as well as explicit bounds on the control signals. The difference from parametric optimization, as described in Section 15.3, is that no parameterized structure is assumed. Instead, the optimum among all possible controls is sought. The control can be represented either as an open loop signal or as a feedback from the state. This methodology is described in Chapter 18.
15.5
State Feedback and Observer
Several of the methods we have mentioned lead to a state feedback solution, e.g., exact linearization and optimal control. If only an output signal is measured it is natural to use the same method as in the linear case, i.e., feedback from an observer. The structure is, in principle, the same as in the linear case, see Section 8.4, but the feedback and the observer contain nonlinear elements. Let us assume that the system to be controlled is
x
f(x,u),
y h(x)
(15.2)
and that we have calculated a control law
u
= e(x)
(15.3)
such that the closed loop system
x = f(x, e(x)) has the properties we want. In analogy with the linear case, we would like to construct an observer, i.e., a dynamical system with u and y as inputs and a state with the property that the obseruer error
x
xx
15.5 State Feedback and Observer
379
tends to zero (in the absence of disturbances and model error). There are several approaches to the construction of such an observer.
Nonlinear Filter If the disturbances can be described as stochastic processes it is natural to view the calculation of i; as a filtering problem. The filter is constructed to minimize some criterion, like the expectation of the squared prediction error. For linear systems with Gaussian disturbances this approach leads to the Kalman filter. For nonlinear systems state variables and outputs usually are not Gaussian even if the disturbances are. Then it. is not enough to calculate only means and covariances, as in the Kalman filter. Instead, it is necessary to compute the conditional expectation of the state, given the measurements. Mathematically, the problem can be solved, but the solution leads to a partial differential equation and a complexity which is too high to be realistic in on line applications. An additional complication is that the combination of an optimal filter and an optimal state feedback usually does not give the overall opthnal solution. This is in contrast to the linear case where the overall optimal solution can be separated into an optimal state feedback and a Kalrnan filter (the Separation Theorem, see Theorem 9.1).
Some Different Approaches to Observers The simplest observer is the model itself:
x f(x,u)
(15.4)
If x(O)
x(O) we have, in the absence of modeling errors and disturbances, that x(t) = x(t) for all positive t. In favorable situations f can be such that the estimation error goes to zero even if x(O) =1= x(O). Normally, however, one has to make some correction based on the measured output. A natural possibility is to make an additive correction ill analogy with the linear case.
i: = /(x, u) + ]((y 
h(x))
(15.5)
There are different strategies for choosing ](. One possibility is to compute the linearization of the system around an equilibrium and then take a]( which gives good dynamics for an observer based on the
3BO
Chapter 15
Controller Synthesis for Nonlinear Systems
linearization. This will give a constant I( which guarantees the observer error to tend to zero close to the equilibrium. Another possibility is to use the present value of x to make a linearization and then calculate a Kalman filter based on that linearization. If this is done at every time instant (in practice at every sanlpling time) the result is an extended ](alman filter. A complication is that it is normally not possible to use the formulas for the Kalman filter which were presented in Chapter 5. This is because these formulas assume time invariance and stationarity. Since the linearization point given by x varies, it is necessary to use the Kalman filter equations for the timevarying case. They are found, e.g., in Anderson & Moore (1979). For systems with the special structure (15.6)
where Xl is the first component of x, the following observer structure can be used
i
= f(y, u)
+ Ax + I({y 
(15.7)
Cx)
where C = [1 0 ... 0]. The observer error equation ~ = {A  ](C)x
x
X 
x then satisfies the (15.8)
and if the pair A, C is observable, ]( can be chosen so that the observer error tends to zero exponentially. The observer is thus globally convergent (in the absence of modeling errors and disturbances). The structure (15.6) is of course very special. However, for systems not having this structure it is sometimes possible to find a nonlinear transformation of the state and the output which leads to a description of the form (15.6). The ideas are related to exact linearization which will be described in Chapter 17. Mathematically, it is however fairly' complicated and we refrain from a detailed discussion here.
SOlne Properties and Problems One problem using nonlinear observers is to guarantee stability. Locally, this can often be done using Theorem 12.1 for a linearization of the observer. With the exception of the methodology of (15.6)(15.8) above, it; is usually not possible to guarantee convergence from
15.5 State Feedback and Observer
381
arbitrary initial conditions. In some cases it is possible to show such stability with the help of Lyapunov functions. It is also noteworthy that there is no guarantee that a good state feedback combined with a good observer will give a good overall result (essentially independently of the interpretation of Ltgood"). We noted above that the principle of separation is not valid for nonlinear systems. As far as stability is concerned, it is possible to construct examples where a state feedback stabilizes globally, but where the global stability is destroyed by an observer, even if this observer has arbitrarily fast exponential convergence. However, the following local result holds. Let the controlled system be given by (15.2) with a linearization around the origin according to Theorem 11.1, given by
± = Ax + Bu, A = Jx(O, 0),
y
B
=
Cx Ju(O, 0),
C
= hx{O)
(15.9)
The closed loop systeln with an observer according to (15.5) is then given by
± = J(x,u) y h(x) §; = J(5:" u) + I((y  h{x))
{IS. lOa) (15.10b)
u = I!(x)
(15.10d)
Setting L
d [x]
(15.10c)
fx{O), the linearization of (15.10) is
dt i; 
[A I(C
A
BL ] [x] BL  I(C i;
(15.11)
This is the same result as we would obtain if we made a linear observer design directly for (15.9). In the same way as we did in the linear case, we can conclude that the poles of (15.11) are the poles of A BL and A  I(C. If these poles lie in the left half plane we can use Theorem 12.1 to draw the conclusion that the combination of state feedback and observer (15.10) has the origin as an asymptotically stable equilibrium. Furthermore, using the reasoning of Chapter 13 we can draw the conclusion that the properties of (15.10) close to the origin are well described by (15.11).
382
Chapter 15
15.6
Controller Synthesis for Nonlinear Systems
Comments
Main Points of the Chapter • A common methodology is to use linear methods for controller design, but to verify the resulting design with a nonlinear modeL • There are a number of nonlinear design methods  Nonlinear IMC.  Parametric optimization.  Model predictive control.  Exact linearization.  Optimal control. • Several of the nonlinear design methods lead to a configuration with a nonlinear feedback and a nonlinear observer.
Literature The literature on MPC, exact linearization and optimal control is described in the relevant chapters. General overviews of nonlinear control design are given in Leigh (1983), Slotine & Li (1991), Atherton (1982) and Cook (1994).
Software For parametric optimization MATLAB has the NONLINEAR CONTROL DESIGN BLOCKSET. Software for MPC, exact linearization and optimal control is described in the relevant chapters.
Chapter 16
Model Predictive Control 16.1
Basic Idea: Predict the Output
An important property of a model is its predictive power, i.e., it gives us the possibility to predict future values of interesting variables. A natural use of a model for control design is to calculate expected future values of the controlled variables as a function of possible control actions. With this knowledge, it is possible to choose a control action which is the best one according to some criterion. More fonnally we can proceed as follows L At time t compute or predict a number of future outputs f)(t + kit), k = 1, .. " li1. They will in general depend on future
inputs u(t time t.
+ j),j
= 0,1, .. " N and on measurements known at
2, Choose a criterion based on these variables and optimize it with respect to u(t + j),j = 0,1, ... , N. 3. Apply u(t) to the physical plant. 4. Wait for the next sampling instant t
+ 1 and go to
1.
This is a very general and flexible method. It is of great value that it is easy to include in the criterion realistic constraints on the magnitude and rate of change of the control variables, The method is called model predictive control, (MPC) and we will discuss various aspects of it in this chapter.
384
Chapter 16
16.2
Model Predictive Control
kstep Prediction for Linear Systems
Usually, discrete time or sampled data models are used in MPC. There are several ways of calculating the predicted future outputs, depending on approximations and noise models used. We first consider linear system dynamics, The most comprehensive approach is to use a state space model with a realistic noise model according to Chapter 5: x(t + 1)
+ Bu(t) + NVI (t) Cx(t) + V2(t)
(16.1a)
Ax(t)
y(t) =
(16.1b)
The state is estimated using an observer or a Kalman filter according to Section 5.8 and we use x(t) = x(tlt) to denote the estimate of the state at t, based on y(k), k ~ t and u(k), k < t. Since VI and V2 are unpredictable, we get the estimated future states from x(t + k + lit)
= Ax(t + kit) + Bu(t + k),
k
= 0, 1,...
(16.2)
and the predicted outputs are
y(t + kit)
=
Cx(t + kit)
(16.3)
These equations show that y(t + kit) is a linear function of i(tlt) and + j),j = 0, .. " k  1. In principle, we can write
u(t
(16.4a)
where Dm and Dx are known matrices and where
fJ(t + Mit)]
yt=
[
y(t U(t
[
~ lit)
7Nl]
(16.4b)
(16.4c)
u(t)
Note that • If N < M 1 (which is common) we must have a rule for choosing u(N + 1)" .. , u(M 1). The most common one is to take the control signal equal to the last computed value u(N) . • Often (16.4) is written in terms of a Ut based on the differences Llu(t) = u(t)  u(t  1).
16.3 The Criterion and the Controller
16.3
385
The Criterion and the Controller
Usually a quadratic criterion is chosen
F(Ut ) = ytTQyyt + U[QuUt
(16.5)
Since yt is a linear (affine) function of Ut , this criterion can easily be minimized analytically, and we get a solution of the form
u(t) = Lx(tlt)
(16.6)
for the last component of Ut . This is a linear controller.
Optimization with control constraints The great advantage of the MPC approach does not lie in the solution of (16.6)  for large values of At and N this is more easily done using the techniques of Chapter 9. The point is instead that it is easy to handle constraints of the form
lu(t)l :::; Cu
and
Iy(t)l:::; C y
which are almost always present in applications. We have seen control constraints in Examples 1.2, 11.1, 14.2 and 15.1. The problem of minimizing the quadratic criterion (16.5), V(Ut ) under a linear constraint (16.7)
has no explicit solution, but it is a convex quadratic programming (QP) problem, for which very efficient numerical methods exist. This problem has to be solved online at each sample, but for normal problem sizes and sampling speeds this is no problem. In chemical process control, where this approach is common, the sampling interval can be on the order of minutes. Note that the result is a nonlinear controller despite the fact that the model (16.1a) is linear, since we take the constraints (16.7) into account.
Choosing the Horizons ]v! and N The output horizon M is usually chosen to cover a typical settling time for the system. We want to loole past the transient of the system response to the input. The control signal horizon N is usually chosen to be significantly shorter it determines the size of the optimization problem.
386
Chapter 16
Model Predictive Control
Example 16.1: A DC Motor Consider the DC motor of Example 10.1. With the sampling interval T::::: 0.15 the sampled state space description is according to (4.16) (Note the factor 20 in the numerator in Example 10.1.) x(t
+ T)
::::: Ax(t)
+ Bu{t)
y(t) ::::: Cx{t) A::::: [1
o
(16.8)
0.1393] B ::::: [0.2142] C ::::: [1 0.8607 ' 2.7858 '
0]
We want the output to equal the reference value r and we choose a criterion of the form (16.5), but without a penalty on u:
where R is a vector whose elements are all equal to r. We take .NJ ::::: 8, which approximately corresponds to the system time constant (8·0.15 = 1.2) and N = 2. We assume that the control values after t + N are chosen equal to zero, since there is an integrator in the system. This gives
1J(t + 8 I t)] y(t + 71t)
Yt : : :
[ iJ(t
+ lit)
=
CAB] CA7
..
[ . CA
x(t)
+
[CA6B CAsB
.. .
0
CA7B] CA6B
:
u(t+1)
[U(t)]
= Hx(t)
+ SU,
CB
The criterion can be written V(Ud =
IIHx(t) + SUt  RI12
For a given value of x(t) and r this is a quadratic criterion in Ut with a minimum in
ut:::::
_(STS)lSTHx(t)
= 
+ (STS)lSTR
[;27~~~2 ;;~5~~~5] x(t) + [!27~g~2] r = 2.4962  [ 2.7702
0.1825] [y(t) 0.5115 X2(t)
r]
16.3 The Criterion and the Controller
387
Using the MPC principle, u(t) is now calculated from this formula giving the state feedback
u(t)
=
[2.7702 0.5115] [yet)  r
X2(t)]T
This control law gives a closed loop system with poles in 0 and 0.1576. In Figure 16.1a it is shown how it works in the presence of the control variable constraint lu (t) I ~ 1.
A major point of MPC is, however, that it is possible to take the input constraint into account by minimizing V(Ut ) under the constraints lu(t)1 ~ 1, lu(t + 1)1 ~ 1. Note that this is not the same thing as bounding the value that minimizes V(U t ). This is shown in Figure 16.1c. It illustrates the level sets of the criterion V(Ut ) which are ellipses around Ut with the matrix ST S = [15.3707 13.0423] 13.0423 11.2696
defining the major and minor axes. The minimizing value of u(t) is attained when the square defining the constraints just touches the lowest level set, Le., approximately 0.4. The minimizing value without constraints is u(t) 2.6490. Figure 16.1b shows the performance of the controller which minimizes Y(Ut} under control signal constraints. We note that it performs considerably better by "knowing" that it cannot use large negative values to brake, but instead changes sign of the control signal earlier. The computation of the minimizing u under constraints can be done using the following straightforward IvIATLABcode. (8 and H are the matrices defined above and r = 0)
» L=8\H; » u=L*Xi » if max(abs(u»>1 » 81=8(: ,1) ;82=8(: ,2) ;kk=Oj » for u1=[1,1] » kk=kk+1; » u2=(S2\H)*xS2\81)*ul; » if u2>1,u2=1;end,if u21,ul=1;end,if u1.(t),no), uEU
0 ~ t ~ tf
(18.35a)
where H is given by {18.34}, and>' satisfies ,X(t) =  Hx(x* (t), u* (t), >.(t) , no)T
(18.35b)
>.(tj)T = no.(tj),no) = no. will not be unique. If the theorenl is satisfied by one choice of them, it will also be satisfied if they are all multiplied by the same nonnegative number. All cases where no > 0 can (by multiplying no, j.L, and >. with 1/11,0) be normalized to the case no = 1 and the formulation given by Theorem 18.3. There are really only two choices of 'no: no = 1 (the normal case) and 'fLO = 0 (the abnormal case). From Theorem 18.1 we see that optimal control problems with fixed final time, and no terminal constraints, are always normal. There are no general and silnple criteria for normality of optimal control problems. In many cases one can only show nonnality by carrying out the calculations for no = 0 and verifying that there is no solution. There is an important corollary of Theorems 18.3 and 18.4.
~C; Corollary 18.1 Let the conditions of Theorem 18.3
18.4 be sat'isr: fled. Then the Hamiltonian is constant along an extremal. 01'
(f~ Proof: We show the corollary for the special case when the control signal :,' lies in the interior of U. Since H is minimized we have Hu = 0 along an
Chapter 18
420
Optimal Control
extremal. This gives the relation
~H == Hxx + H>.~ + Hv.u == Hxf 
fTH'[ = 0
o Example 18.2:
Optimal Heating
We consider an optimal heating problem. Let T be the temperature of an object that is heated with the power P. We assume that the environment has the temperature zero) and that the heat loss is proportional to the temperature difference. We then get
T=PT where, for simplicity, we have assumed that all physical constants are equal to 1. Let the power be bounded by
Consider the problem of raising the temperature one unit in one unit of time, i.e., T(O) = 0,
T(I)
=1
with minimal energy consumption, i.e., minimize
11
P(t)dt
The Hamiltonian is
H
= noP+)..P)..T
The equations (18.32) for A are" == A, A(I) ILl et  1 . Introducing u(t) = no
we can write H
pet)
+ A(t) =
no
= a(t)P 
==
Jil with the solution
)..(t)
=
+ Jilet1
)"T, so that (18.31) gives
= { 0,
aCt) > 0 Pmax1 aCt) < 0
Let us now consider the different possibilities. L Jil > O. This means that aCt) > 0 for all t, giving pet) = 0 during whole time interval. Then T(l) = 0, which does not satisfy the tarrnll"\11 constraint. This case must thus be rejected.
421
18.2 The Maximum Principle
2. 1£1 = O. This means that A(t) == O. Since (no /iT] must not be zero, we get no > 0, which means that. u(t) > 0 for all t. This case must then be rejected for the same reason as the previous one. 3. 1£1 < O. In this case u(t) is strictly decreasing. There is then at most one time instant tl such that (j(t1) = O. Since case 3 above is the only possible one, we conclude that an extremal must be of the form
pet)
= { 0,
0
Pmax,
~t~
it < t
~
t1
(18.36)
1
Possibly tl is outside the time interval 0
~
t
~
1 in which case P
==
0 or
P==Pmax .
For P given by (18.36) we get
r Jtl 1
1 = T(l) =
e(tT) Pmaxdr
= (1 e l +t1 )Pmax
Solving for tt gives t1
= 1 + 1n(1 
The condition (j(h) /i1
P;;;'!x)
= 0 then gives
no
=
(18.38)
Since Pmax is positive, we automatically get il Pmax
(18.37)
;:::
~
1. We see that it ;::: 0 if
1 1 e 1
(18.39)
This condition also implies that 1£1 < 0 in (18.38). If there is equalit.y in (18.39), then T(l) = 1 is achieved by taking pet) Pmax1 0 ~ t ~ 1. This is the lowest value of Pmax for which the optimal control problem is solvable. We conclude that there are two cases. 1. IT Pmax does not satisfy (18.39) there is no choice of pet) which satisfies
the terminal constraints. There is thus no solution to the optimal control problem. 2. If Pmax satisfies (18.39) there is an extremal given by (18.36) with tl defined by (18.37). From (18.38) we see that no # 0 since [no I£T] must be nonzero. The extremal is thus normal. ;@u:!~xaLmlDle
18.3:
DC Motor with Power Constraint
Chapter 18
422
Optimal Control
Let us consider positioning with a current controlled DC motor. A simple model using the current as input signal u is
w== u,
() == w
(18.40)
where (J is the angle of rotation of the motor axis and w the angular velocity. (Note that this model is different from the usual one where the input is the applied voltage. Instead of one time constant and one integrator we now have a double integrator.) Now suppose that the motor axis should be turned from one position to another in the shortest possible time. This gives the end point constraints (18.41) and the following criterion to minimize
/.'J 1
dt
= tf
(18.42)
Of course, there will not be a well defined problem if we do not have any restrictions on the choice of u. This is because the system is controllable, which means that it is possible to reach any state in an arbitrarily short time. Let us assume that heating of the motor is the limiting factor. Then it is natural to introduce a constraint of the form (18.43)
!
since the amount of power being lost as heat is proportional to u 2 • To get a problem of standard type we can introduce an extra state W, satisfying (18.44) The condition (18.43) then becomes (18.45) Since it is clear, for physical reasons, that this constraint should be active, we ; can put (18.46) .:
We now have an optimal control problem specified by (18.40), (18.44), (18.41), ' (18.46) and (18.42). Using the general notation of (18.25) we get
18.2 The Maximum Principle
= 0,
Xo
L = 1,
423
¢= 0
The Hamiltonian is
= no + AIU + A2W + A3U2
H
(18.47)
and u has to be chosen to minimize this expression. From (18.32), A has to satisfy
= A2,
~l
~2
= 0,
~3
=0
(18.48)
We see that A2 and A3 are constant. After solving for AI, we get
= C1 
Al
C'l,t,
A2
= C2 ,
A3
= C3
for some constants C1 , C2 and Ca. The minimization of H in (18.47) is well posed if the coefficient of u 2 is positive, i.e., Aa C3 > O. We will only consider this case. The minimization of H gives
=
8H 8u
= C1 
C2 t
+ 2C3 u = 0
i.e., a control that varies linearly with time. By renaming the constants we can write
= A+Bt A = Ct/(2Ca),
u
(18.49)
B = C2 /(2C3 ). where equations (18.40) then becomes W
= At + Bt 2 /2,
The solution to the differential
() = At2 /2 + Bt 3 /6
The terminal constraints give
A
= 6/t1,
B
= 12/t}
(18.50)
The third state is given by
w=
t' ((:;1)' +1)
condition lV(t!) = Wo gives the following value oftJ. tJ
= ( 12) 1/3 TVo
l:iutlsti'tuting into (18.49) via (18.50) gives the extremal
u=
wo C£
t), tf
= (~f3
Figures 18.2a and 18.2b the control signal and state variables are shown the case = 12.
424
Chapter 18
Optimal Control
s'
(a) The control signal u (solid) and the accumulated heat W (dashed).
(b) The angle 0 (dMhed) and the angular velocity w (solid).
Figure 18.2: Time optimal control of a DC motor under power constraint.
18.3
Solution of the Goddard Rocket Problem
Let us now discuss the solution of the Goddard rocket problem, see Equations (18.1). The problem is not exactly of the type we have discussed, due to the inequality condition m(t f) 2:: mI. Since it is ' fairly obvious that it is pointless to have fuel left at the final time, we can replace it with the equality constraint m(tf) = mI. The Hamil tonian is H
= Al
UD(v,h) (
m
 9
)
+ A2 V 
A3'YU
The Aequations are
A2(tf) = no A3(tf)=/Ll
where Dv = aD / av, DIt = aD / ah. Hamiltonian is
The coefficient of u in the
18.3 Solution of the Goddard Rocket Problem
425
The minimization of the Hamiltonian is achieved by the following choice ofu.
u(t) =
{o
U max
cr(t) > 0 a(t)
with ¢ + JLT"p. With this change, we continue the calculations as we did in the derivations of Theorems 18.1 and 18.2. This gives directly (lS.31) and (18.32) of Theorem lS.3. To get (18.33) we have to consider what happens if t f is varied. We will do this for the case L = O. Since x at the final time has a velocity of ±* = f(x*(tf LU*(tf )), a change of t f with the amount ±€ gives the change ± Ef(x*(t f), u* (t f)) in x* (t f) (to first order). The first order changes in ¢> and "p are then given by ± fV, where
This vector has to be taken into the cone I( of Figures lS.6 and 18.7. Equation (18.91) then gives ± n T v ~ 0 => n T v = O. For the normal case this relation can be rewritten ¢>t(tf, x*(tf))
+ JLT "pt(tfl x*(tf)) + (l/lx(tfl x*(tf)) + p,T ~x(th x*(t f )))f(x* (tf), u· (tf))
=0
Using the final value of A from (lS.32) gives
which is (18.33) for the case L = O. The generalization to nonzero Lfunctions is done in analogy with Theorem 1S.2. We have thus shown the Maximum Principle for normal problems, Theorem 18.3. The general formulation, Theorem 18.4, is shown by doing the calculations above for general no and the general definition of H;
H(x, u,).., no) = noL(x, u)
+)..T f(x,
u)
Chapter 19
Some Final Words The past 450 pages have dealt with quite a bit of theory and methodology. Which are the central results? What should one remember of all this? How to approach a practical Let us here summarize the core material of the boole
Limitations Capacity Limitations. An important message is that there are inherent performance limitations in all control systems, that do not depend on the controller or on how it is calculated. These limitations might be of practical nature, and are then related to bounds on the control signal and its rate of change. This Inay prevent us from making the system as fast as desired, or obtaining reasonable disturbance rejection. Scaling the variables and making backofanenvelope calculations as in Chapter 7 is the natural start of any controller design in practice. Fundamental Limitations. The lirnitations may also be of principal character, like that the sensitivity may not be arbitrarily good, no matter what input amplitudes are allowed. In particular if the system is unstable and/or nonminimumphase, there are strong basic performance limitations. The nonminimum phase property is particularly treacherous in the multivariable case, since it is not visible from the individual transfer functions. The discussion and results of Chapter 7 are therefore of central impo~tance. There are cases where theoretically impossible performance specifications have been given, and where considerable time has been spent on trying to find an _ _ _ _ _ .1._
L 1 ____
.1.. _ _ 11
__
Chapter 19
452
Some Final Words
Disturbances and Noise. Another fundamental problem in the control design is related to the basic difference between system disturbances and measurement disturbances. The impact of the former on the controlled variable z should be damped or eliminated by the controller, while the latter's influence on the measured output y should be neglected, if possible, when u is fornled. (If not, the measured output y would follow r, which means that the control error e would be equal to the measurement error n.) It is thus necessary to strike a balance in the treatment of measurement and system disturbances. For a linear system we have y = z+n=
Zu
+ Zw + n
where Zu is that component of z that originates from the control, and Zw originates from the system disturbance. Since w is not measured, there is no possibility to distinguish between these two contributions to 11, other than by some a priori information. This may be of the form that "the measurement noise is primarily of high frequency, while the system disturbances are dominated by low frequency signals" or "there is a constant error is the sensors, due to misalignment of the gyros". The observer and the Kalman filter of Chapter 5 allow a good distinction between the disturbances, provided the model picks up their essential properties.
When is the Theory Required? Multivariable Theory. A multivariable system that is ahnost decoupled, so that the different loops do not interact very much, can of course be well controlled by simple regulators, designing one SISO controller loop at a time. With strong cross couplings present, we have seen that it may be quite difficult to understand why such a controller does not work. It is then necessary to treat the multivariable system as one unit, st udying the zeros and the singular values for the entire system, and not only for the individual loops. Nonlinear Theory. All reallife systems are nonlinear, but this may be manifested in different ways. Suppose the system can be linearized around an equilibrium that varies much slower than the linear dynamics. Then using linear design methodology will often work well. Aircraft control is a typical example. The linearized dynamics (as in
453
Example 2.3) strongly depends on velocity and altitude, but these vary quite slowly compared to the time constants of the system. In other cases, the nonlinearity is essential, and then the nonlinear theory has to be used. See below.
Sampled Data Theory. It is important to realize that the sampling and time discretization give phase losses in the loop. This may mean that a controller that has been designed using continuous time models, may give worse stability margins than expected when it is implemented digitally. If the implementation shows some problems, the sampled data, closed loop system, according to Section 6.7 should be analyzed. The result may be that it is necessary to directly synthesize the sampled data controller using a discrete time description of the plant. Today continuous time design dominates, together with fast sampling, except for model predictive control, which by nature is sampled.
How to Choose the Design Method? We have treated a number of different design methods in this book, and it is not easy to know which one to apply in a given case. It is also true that there is no "best" method for a given problem. The basic principle, as in all engineering work, is "to try silnple things first". If a simple PIDregulator gives satisfactory behavior, there is no need to bring in the llCX)machinery.
Linear Systems. For a multivariable system the first task is to find out the strength of the cross couplings between the signals. This can simply be done by studying how steps in the different control inputs affect the outputs. For better understanding it may be necessary to set up and analyze a lllodel of the system, as described in Part II. If sinlple PIDregulators are believed to be sufficient for the control, decoupled or decentralized control will be a good starting point. For better performance, the systematic design methods of Chapters 9 and 10 Inay be easier to use. Perhaps, the most important message about linear controllers is to draw and scrutinize (and understand what is seen!) the singular values of the different closed loop transfer functions, regardless of how the controller was designed. The resulting system lllUSt also be simulated with all reallife constraints like input saturation and rate limitations,
etc.
454
Chapter 19
Some Final Words
Nonlinear Systems. For nonlinear systems there are several approaches. When the system can be well described by a linearized system, with an operating point (equilibrium) that slowly varies, it is a nonnal approach to use linear design techniques for a number of typical operating points. Then the controller that corresponds to the closest operating point is used. This is the standard solution in, for example, aircraft controL Linear interpolation between adjacent controllers can also be used. This method is known as Gain scheduling. If the nonlinearity primarily is caused by the actuator, like saturation, rate limitation, hysteresis, etc., it is hnportant to analyze what this may lead to. The circle criterion and describing functions can be used for that. If the nonlinearity is essential in the sense that the limitation is active most of the time, the theory of Chapter 18 may give guidance for the controller construction. So called fuzzy control lllay be a "soft" way of implementing such nonlinear features in the controller. Otherwise, model predictive control is an increasingly popular method to handle input optimization in the presence of essential constraints. The third and most difficult case is that the nonlinearity is part of the dynmnics, and cannot be simplified by linearization, since the operating points change too fast. If a good model is available, like, e.g., for industrial robots, aircraft etc., it is natural to check if exact linearization can be used. A reasonable model can also be the basis of model predictive control. In general, there is also industrial tradition that affects which design method is used. In process industry, for example, PIDthinking dominates, now more and more complemented by model predictive control. In the aerospace industry, the rnethods of Chapters 9 and 10, as well as optimal control have seen a breakthrough with the latest generation of prod ucts.
Bibliography Anderson, B. D. O. (1967), 'An algebraic solution to the spectral factorization problem', IEEE Trans. Autom. Control 12, 410414. Anderson, B. D. O. & Moore, J. B. (1979), Optimal Filter'ing, PrenticeHall, Englewood Cliffs, NJ. Anderson, B. D. O. & Moore, J. B. (1989), Optimal Control: Linear Quadratic Alethods, PrenticeHall. Astroln, K. J. & Wittenmark, B. (1997), Computer'Controlled Systems, Theory and Design, 3rd edn, PrenticeHall, Englewood Cliffs, NJ. Astroln, K. J., Hagander, P. & Sternby, J. (1984), 'Zeros of sampled systems', A utomatica 20, 3138. Atherton, D. P. (1982), Nonlinear Control Engineer'ing, Van Nostrand Reinhold, New York. Bode, H. W. (1945), Network Analysis and Feedback Amplifier Design, Van Norstrand, New York. Bracewell, R. N. (1978), The Fourier Transform and Its Applications, McGrawHill, New York. Bristol, E. H. (1966), 'On a new measure of interactions for multivariable process control', IEEE Trans. Auiom. Controll1, 133134. Bryson, Jr., A. E. & Ho, Y. C. (1975), Applied Optimal Control, John Wiley, New York. Camacho, E. F. & Bm'dons, C. (1995), lvfodel Predictive Control in the Process Industry, Springer Verlag, New York.
456
BIBLIOGRAPHY
Churchill, R. V. & Brown, J. W. (1984), Complex Variables and Applications, 4:th edn, McGrawHill, New York. Cook, P. A. (1994), Nonlinear Dynamical Systems, Prentice Hall, Englewood Cliffs, NJ. Dorf, R. C. & Bishop, R. H. (1995), Modern Control Systems, AddisonWesley, Reading, MA. Doyle, J. C. & Stein, G. (1981), 'Multivariable feedback design concepts for a classical modern syntheis', IEEE Trans. A utom. Control 26,416. Fleming, W. H. & Rishel, R. W. (1975), Deterministic and Stochastic Optimal Control, SpringerVerlag, New York. Franklin, G. F., Powell, J. D. & AmamiNaeini, A. (1994), Feedback Control of Dynamic Systems, 3rd edn, AddisonWesley, Reading, MA. Franklin, G. F., Powell, J. D. & Workman, M. L. (1990), Digital Control of Dynamic Systems, 2nd edn, AddisonWesley, Reading, MA. Freudenberg, J. S. & Looze, D. P. (1988), Frequency Domain Properties of Scalar and Multivariable Feedback Systems, SpringerVerlag, New York. Friedland, B. (1988), Control System Design, McGrawHill, New York. Glover, K. & McFarlane, D. (1989), 'Robust stabilization of normalized coprime factor plant descriptions with Hoo bounded uncertainty" IEEE Trans. Autom. Control 34,821830. Golub, G. H. & Loan, C. F. V. (1996), Matrix Computations, 3rd edn, Johns Hopkins Univ. Press, Baltimore. Heymann, M. (1968), 'Comments on pole assignment in multiinput linear systems', IEEE Trans. Autom. Control 13, 748749. Horn, R. A. & Johnson, C. R. (1985), Matrix Analysis, Cambridge University Press. Horowitz, I. M. (1993), Quantative Feedback Design Theory, QFT Publications, Boulder, Colorado.
BIBLIOGRAPHY
457
Isidori, A. (1995), Nonlinear Control Systems, SpringerVerlag, New York. James, H. M., Nichols, N. B. & Phillips, R. S. (1947), Theory of Servomechanisms, McGrawHill, New York. Jury, E. I. (1958), SampledData Control Systems, John Wiley, New York. Kailath, T. (1980), Linear Systems, PrenticeHall, Englewood Cliffs, NJ. I{alman, R. E. (1960a), 'Contributions to the theory of optimal control', Bol. Soc. Matern. Mex. pp. 109119. Kalman, R. E. (1960b), LA new approach to linear filtering and prediction problems" Trans ASME, Ser. D. Journal Basic Eng. 82,2445. Kalman, R. E. (1960c), On the general theory of control systems, in 'Proc. First IFAC Congress', Vol. 1, Butterworths, Moscow, pp. 481492. Kalman, R. E. & Bucy, R. S. (1961), 'New results in linear filtering and prediction theory', Trans ASME, Ser. D. lournal Basic Eng. 83,95107. I(halil, H. K. (1996), Nonlinear Systems, McMillan, New York. I( wakernaak, H. & Sivan, R. (1972), Linear Optimal Control Systems, Wiley Interscience, New York. Leigh, J. R. (1983), Essentials of Nonlinear Control Theory, Peter Peregrinus, London. Leitmann, G. (1981), The Calculus of Variations and Optimal Control, Plenum Press, New York. Ljung, L. (1999), System Identification  The01'y for the User, 2nd edn, Prentice Hall, Upper Saddle River, NJ. Ljung, L. & Glad, T. (1994), Modeling of Dynamic Systems, Prentice Hall, Upper Saddle River, NJ.
458
BIBLIOGRAPHY
Lyapunov, A. M. (1992), Stability of Motion, Taylor and Francis, London. Maciejowski, J. M. (1989), Multivariable Feedback Design, AddisonWesley Publishing Company, Reading, MA. Minkler, G. & Minkler, J. (1993), Theory and Application of ](alman Filtering, Magellan Book Company, Palm Bay, FL. Moore, B. (1981), 'Principal component analysis in linear systemp! Controllability, observability and model reduction', IEEE Trans. Autom. Control 26, 1731. Morari, M. & Zafiriou, E. (1989), Robust Process Control, PrellticeHaU, Englewood Cliffs, N J. Newton, G. C., Gould, L. A. & Kaiser, J. F. (1957), Analytical Design of Linear Feedback Control, Wiley, New York. Nijmeijer, H. & van der Schaft, A. (1990), Nonlinear Dynamical Control Systems, SpringerVerlag, New York. Oppenheim, A. V. & Schafer, R. W. (1989), Discrete Time Signal Processing, PentlceHall, Englewood Cliffs, NJ. Papoulis, A. (1977), Signal Analysis, McGrawHill, New York. Prett, D. M. & Garcia, C. E. (1988), Fundamental Process Control, Butterworths, Stonehan, MA. Ragazzini, J. R. & Franklin, G. F. (1958), SampledData Control Systems, McGrawHill, New York. Safonov, M. G. (1980), Stability and Robustness of Multivariable Feedback Systems, MIT Press, Cambridge, MA. Seborg, D. E., Edgar, T. F. & Mellichamp, D. A. (1989), Process Dynamics and Control, John Wiley, New York. Seron, M. M., Braslavsky, J. H. & Goodwin, G. C. (1997), Fundamental Limitations in Filtering and Control, SpringerVerlag, New York. Slcogestad, S. & Postlethwaite, 1. (1996), Multivan:able Feedback Control, John Wiley, New York.
BIBLIOGRAPHY
459
Slotine, J.J. E. & Li, W. (1991), Applied Nonlinear Control, PrenticeHall, Englewood Cliffs, NJ. Stein, G. & Athans, M. (1987), 'The LQG/LTR procedure for multivariahle feedback control design" IEEE Trans. Autom. Control 32, 105114. Thompson, J. M. T. & Stewart, H. B. (1986), Nonlinear Dynamics and Chaos, Wiley. Tsypldn, Y. Z. (1958), Theory of Impulse Systems, State Publisher for Physical Mathematical Sciences. Van Dooren, P. (1981), 'A generalized eigenvaJue approach for solving Riccati equations', SIAM Journal Sci. Stat. Compo 2, 121135. Verhulst, F. (1990), Nonlinear Differential Equations and Dynamical Systems, SpringerVerlag, New York. Vidyasagar, M. (1993), Nonlinear Systems Analysis, PrenticeHall, Englewood Cliffs, NJ. Wiener, N. (1949), The Extrapolation, Interpolation and Smoothing of Stationary Time Ser'ies, John Wiley, New York. Wong, E. (1983), Introduction to Random Processes, SpringerVerlag, New York. Youla, D. C., Jabr, H. A. & Bongiorno, J. J. (1976), 'Modern WienerHopf design of optimal controllers. Part II: The multivariable case.', IEEE Trans. Autom. Control 21, 319338. Zhou, Ie, Doyle, J. C. & Glover, K. (1996), Robust and Optimal Control, PrenticeHall, Englewood Cliffs, NJ.
Index 1DOF controller, 217 2DOF controller, 216
MIMO, 195 Bode's relation, 177, 178
abnormal control problem, 418, 448 additivity, 308 adjoint equation, 413 affine, 314 aircraft speed control, 394, 403 aircraft angles, 34 algebraic Riccati equation, 246 alias, 139 amplification, 59 amplitude curve, 59 amplitude dependent gain, 359 analytic function, 177 anti causal, 114 argument principle, 73 asymptotically stable systems, 57
calibration error, 121 causal, 10, 114, 242, 243 CayleyHamilton's theorem, 70, 135 center, 344 circle criterion, 334 closed loop system, 11, 147, 149 preferences, 155 complementary sensitivity function, 149, 156 limitation, 184 state feedback, 235 computed force, 394 computed torque, 394 configuration, 215 constraint control, 385, 410 terminal, 410 control affine, 314 control error, 12, 148 control input, 1113 control object, 11, 84 controllability, 45 discrete time, 69 PBHtest, 47 controllability Gramian, 64 controllability matrix, 45 controllable mode, 46 controlled variable, 11, 13
backlash, 368 balanced state representation, 66 model reduction, 67 band width, 159 bangbang, 429 bilinear transformation, 84 Bode plot, 58 Bode's integral theorem, 180, 260
Index controller, 11 synthesis linear, 215 nonlinear, 371 controller canonical form, 46, 402 covariance matrix, 104, 110 criterion integral term, 416 LQ,239 optimal control, 410 cross coupling, 219 crossover frequency, 75 cubic nonlinearity, 366 DC motor, 5, 309 dead time, 231, 232 dead time compensation, 232 dead zone, 367 deadbeat control, 269 deadbeat reconstruction, 135 decentralized controller, 221, 293 decomposition singular values, 60 decoupling, 198, 226 dynamical, 226 steadystate, 226 degrees of freedom, 216 derivation operator, 30 describing function, 358 backlash, 368 cubic gain, 366 dead zone, 367 relay with dead zone, 366 relay with hysteresis, 368 saturation, 367 detectable, 48 diagonal element, 105 direct term, 233 discrete time, 40
461 in controller, 236 in LQGcontroller, 241, 265 in observer, 125, 135 discrete time system, 165 disturbance, 11, 12, 101 disturbance signal, 101 equilibrium, 317 ergodic, 109 error coefficient, 158, 184 exact linearization, 393 inputoutput, 397 state, 402 extended Kalman, 380 extremal, 414 feedback, 147, 216 from model error, IMC, 228 from reconstructed states, 233,288 optimal, 409, 436 optimal LQ, 243 time optimal, 434 feedforward, 12,240, 254 filter Kalman, 126 low pass, 139 nonlinear, 379 presampling, 139 Wiener, 114 final value theorem, 29, 38 focus stable, 344 unstable, 344 Fourier series, 356 frequency fidelity, 308 frequency function, 58, 77 sam pled system, 97 frequency response, 58, 77 frequency weighting, 278
462
friction, 313, 349 fuzzy control, 454 gain, 16, 18, 61 gain crossover, 172 gain margin LQ,259 gain scheduling, 454 global asymptotic stability, 322 GloverMcFarlane's method, 293 Goddard rocket problem, 407 greatest common factor, 52 HamiltonJacobi equation, 437 HamiltonJacobiBellman equation, 437 Hamiltonian, 416 Hermitian matrix, 59 1i oo norm, 15 1i oo control, 285 discrete time, 301 user aspects, 288 1i2control, 284 discrete time, 301 1i2norm, 15, 284 hnpulse response, 28 discrete time, 38, 69 infinite dimensional, 408 infinity nonn, 63 innovation, 129, 138 innovation form, 128 inputLTR, 264 inputoutput stability, 18 instability, 321 performance limitation, 186 internal model controllers with, 1M C, 228 internally stable, 151 interpolation condition, 183, 197 illvariance, 181
Index
invariance under scaling, 308 Jordan normal form, 81, 275 Kalman filter, 126128 discrete time, 137 extended, 380 measurement update, 138 Liederivative, 396 limit cycle, 351 limitation control signal, 174, 192 performance complementary sensitivity function, 184 control bounds, 192 instability, 186 nonminimuIIl phase, 190, 196 sensitivity function, 179 system inverse, 174 linear controller, 147 linear quadratic Gaussian control, 243 linear quadratic control, 243 linearization, 319 load disturbance, 148, 232 load variations, 101 loop gain, 148, 176, 217, 221, 234, 257 LQG, 260 state feedback, 259 loop transfer recovery, 262 low pass filter, 139 LQ control, 243 LQcriterion, 239, 440 cross term, 245, 266 discrete time, 264 LQmethod discrete time, 265
with direct term, 265 LQG control, 243 LTR, 262 LTRmethod input side, 264 output side, 263 Lyapunov equation, 124, 328 Lyapunov function, 324 Lyapunov stability, 21 Markov process, 121 matrix adjoint, 59 complex valued, 59 Hermitian, 59 singular values, 60 unitary, 60 matrix exponential, 54 matrix fraction description, MFD, 31 maximal minor, 50 maximum principle, 410, 418 measured output, 11, 13 measurement disturbance, 13 measurement error, 13, 102 measurement update, 136, 138 MIMO system, 1 minimum phase, 178 minimum realization, 46 minimum time problem, 428 minimum variance control, 271 minor, 49 mode, 44, 4648, 53 controllable, 46, 47 discrete time, 69 observable, 46 uncontrollable, 150 unobservable, 46, 150 model error, 102, 152, 155 model predictive control, 7, 383
model reduction, 67 controller, 302 discrete time, 77 MPC, 7,383 Jlsynthesis, 302 multivariable, 10, 27 multivariable system, 31 node, 341 nonminimum phase, 58, 173, 178, 257, 399 discrete time, 93 MIMO example, 197 performance linlitation, 190, 196 nonlinear filter, 379 nonlinear observer, 404 norm, 60 normal control problem, 418,
448 null space, 113, 132 nUlnerical methods optimal control, 442 Nyquist criterion, 57 discrete time, 73, 74 Nyquist frequency, 83, 139 observability, 45 discrete time, 70 PBHtest,47 observability Gramian, 65 observability matrix, 45 observable mode, 46 observer, 124, 378 discrete time, 134 nonlinear, 379 linear error dynalnics, 380 reduced order, 126, 136 with direct term, 125
464
observer canonical form, 36, 46 open loop control, 409 open loop system, 11 optimal control problem, 408 optimal return, 436 optimality sufficient conditions, 438 orbit, 339 Otto Smith regulator, 232 outputLTR, 263 overshoot, 157 pairing, 221, 222 ParsevaPs equality, 17, 108, 111, 123, 218 PBH (PopovBelevichHautus) test, 47 phase margin, 75 LQ,259 phase plane, 339 center, 344 focus, 344 star node, 342 PIDregulator, 232 piecewise linear, 315 plant, 11 Poisson's summation formula, 139 po~, 49, 50, 56, 113, 132, 158 discrete time, 71 multiplicity, 51 pole placement, 237 pole polynomial, 49 powers of matrices, 54 precompensation, 147 presampling filter, 139 proper, 29 strict, 29, 115 pulse response, 38, 69
Index
Qparameterization, 230 QFT, 277 Quantitative Feedback Design Theory, 277 reduced order observer, 126, 136 reference signal, 11, 284 state feedback, 236 regulator, 11 relative degree, 396 Relative Gain Array, 219 relay ideal, 359 with dead zone, 366 with hysteresis, 368 reset windup, 374 resonance frequency, 159 resonance peak, 159 RGA, 219 Riccati equation, 128, 129, 272, 441 algebraic, 128, 243, 246 discrete time, 138, 265 numerical solution, 246, 272 stationary, 128, 246 right half plane pole, 181 rise time, 157 robustness, 153 LQG, 260 root locus method, 57, 72 saddle point, 342 sampled data controller, 164 sam pled data system, 86 sampling, 37 sampling frequency, 83, 139 sampling instants, 95, 168 sampling interval, 83 sampling theorem, 90 sampling time, 83
Index
saturation, 363, 367, 375 scaling, 451 sensitivity, 153 sensitivity function, 149, 153, 156 input, 149, 152 limitation, 179 LQ,259 state feedback, 235 separation theorem, 243 setpoint, 11 settling time, 157 shift operator, 39 backwards, 39 singleinputsingleoutput systems, 1 singular point, 317 singular probleln, 425 singular trajectory, 425 singular value decomposition, 60 singular values, 60, 61, 285 structured, 302 SISO,10 SISO system, 1 size of state vector discrete tilne, 134 small gain theorem, 18, 155 specifications, 215 spectral density, 106 spectral factorization, 112 discrete time, 132 spectrum, 106 discrete time, 132 stability, 21, 321 asymptotic, 21, 321 criterion, 324 discrete time, 71 global asymptotic, 322
465
inputoutput, 18, ~6 internal, 151 Lyapunov, 21 of solutions, 21 oscillations, 361 stability margin, 75 stability region, 55 discrete time, 71 stabilizable, 48 star node, 342 state feedback, 378 LQ,243 state representation, 31 balanced, 66 controller canonical form, 36 diagonal form, 46 minimal, 46 observer canonical form, 36, 37 solution, 43, 69 state size, 123 state vector, 31 static error, 157 stationary point, 317 structuring, 215 superposition, 308 switching curve, 434 system, 9 closed loop, 147, 155 dynamic, 10 linear, 10 multivariable, 10, 27, 31 noncausal, 10 nonlinear, 10, 312 control affine, 314 piecewise linear, 315 sampled, 86 sampling, 86
466
SISO, 10 static, 10 time continuous, 10 time discrete, 10 time invariant, 10 time varying, 10 system disturbance, 13 system gain, 63 system inverse limitation, 174 systenl property, 57
Taylor expansion, 30, 318 template, 277 terminal constraints, 417 tinle update, 136, 138 timedelay, 231 timevarying, 441 trace, 105 trajectory, 339 transfer function matrix, 28 discrete time, 38 transfer operator, 30 discrete time, 39 from state form, 35 two point boundary value problem, 443 value function, 436 value set, 277 variational equation, 412 vectordifferential equation, 31 weighted quadratic norm, 104 weighting function, 28 white noise, 107, 111, 119 discrete time, 132 Wiener filter, 114 discrete time, 132 windup, 374
Index
Yonlaparameterization, 230, 238
z transform, 38 zero, 51, 53, 58, 151, 158, 262 discrete time, 71 right half plane, 183, 184 zero dynamics, 399 zero polynomial, 52
Index of Examples DC motor 1l2control, 285 1l oo control, 289 extended model, 282 MPC, 386 robustness, 295 sampled control, 167 sampling, 88 saturation, 309, 312, 375 time optinlal control, 433 distillation column decoupled control, 226 model, 223 MPC, 389 RGA, 225 double integrator control, 93 optimal control, 421 reduced order observer, 136 sampling, 87 saturation, 5 generator equilibria, 318 linearization, 318 phase plane, 349 stability, 326 Gripen aircraft disturbance model, 117 LQ control, 248
Index
state model, 33 transfer function matrix, 52 heat exchanger balanced realization, 66 red uced model, 68 state model, 32 transfer function matrix, 35 inventory control disturbance model, 133 LQ control, 268, 269 state model, 41 transfer function, 40 nonminimum phase control, 197 control difficulties, 2 LQ control, 256 poles, 50 RGA, 223 zeros, 52 room heating disturbances, 12 LQ control, 252 model, 118, 120, 121
467