PREDICTIVE CONTROL WITH CON-STRAINTS, J.M. Maciejowski; PearsonEducation Limited, Prentice Hall, London,2002, pp. IX+331, price £35.99, ISBN 0-201-39823-0

For complex processes with time-delay effects,

a non-minimum phase behaviour, unusual dy-

namics or a multivariable system structure,

advanced techniques such as generalized predic-

tive control (GPC) or dynamic matrix control

(DMC) may be required to achieve better control

performance. For example, in the petrochemical

industries, the GPC method [1] has become one of

the most popular Model Predictive Control

(MPC) methods to be implemented. However, in

other industries and in more routine situations,

even though these advanced techniques may

provide superior control, they may not be used.

Implementing model-based control may also

require capital investment to support new hard-

ware and software products and also resources to

train personnel in the operational behaviour of

the new advanced controllers.

There are relatively few books devoted to

the subject of model predictive control. ‘Model

Predictive Control’ by Camacho and Bordons

[2], is one notable textbook contribution to

the Predictive Control bibliography. This is

an improved version of the monograph ‘Model

Predictive Control in the Process Industry’

by Camacho and Bordons [3], which was pub-

lished in the series ‘Advances in Industrial

Control.’ Recently Springer Verlag also pub-

lished the book ‘Applied Predictive Control’ by

Huang, Tan and Lee [4]. However, this review

concerns a new book by Maciejowski which

provides a good presentation of the state of the

art in Predictive Control. The book has been

organized as follows:

Chapter one presents a review of the historical

background of MPC and the motivation for

publishing the book. Also this chapter introduces

the main concepts involved in predictive control

such as the internal model, the reference trajec-

tory, a receding horizon and optimization.

Chapter 2 presents a standard formulation of

predictive control, which assumes the presence of

constraints, and of a multivariable plant. For this

purpose a state space setting is used. Offset free

tracking and stabilization of an unstable model

are discussed in the context of state observers.

Most of the interest in predictive control comes

from its ability to handle constraints. Chapter 3

considers how to solve on-line optimization

problems which arise in predictive control when

constraints are present. A major problem is the

possibility that the optimization becomes infea-

sible, and special strategies are needed for hand-

ling this situation. Chapter 3 includes an

explanation of how this can be done.

As is well known, the only information that

is really needed to implement a predictive

controller is the plant’s step response. Indeed the

original developments of predictive control

assumed a model in the form of a step response

[2, 3]. Some developments like GPC assumed

the model to be in the form of a transfer func-

tion. Chapter 4 deals with formulations of

predictive control based on step response and

transfer function models, and shows how these are

related to the state space setting used in the rest of

the book.

Many variations on the basic formulation of

predictive control are possible. For example, it is

possible to use other criteria than a quadratic one

for finding the optimal future input trajectory.

Also, it is possible to be creative when defining

required performance. These and other possibi-

lities are introduced in Chapter 5.

Nominal stability of the closed-loop system

with predictive control is discussed in Chapter 6.

It is assumed that an exact model of the plant is

available. Some methods such as, using terminal

constraints or distributing coincidence points

evenly along an infinite prediction horizon, are

introduced and discussed to adjust the formula-

tion and the tuning parameters so that closed-loop

stability is guaranteed.

In Chapter 7, the tuning problem of predictive

control is considered. Effect of the prediction

horizon, the time constant of the reference

trajectory and weights in the criterion are

investigated to adjust the controller.

Stability robustness of the predictive control is

addressed in Chapter 8. This issue is still at the

research stage.

In Chapter 9, predictive control is applied to

two well-known systems. First, the Shell heavy-oil

Copyright # 2003 John Wiley & Sons, Ltd. Int. J. Adapt. Control Signal Process. 2003; 17:259–263

BOOK REVIEWS 261

fractionator problem is considered and then the

effect of plant non-linearity is demonstrated by

applying the method to the Newell–Lee evapora-

tor model.

Chapter 10 addresses some wider perspectives

on predictive control such as the possibility of

constraint management within the predictive

control framework, the use of non-linear models

and the potential of the predictive control as a

basis for fault tolerant control systems.

Finally, descriptions of some predictive com-

mercial products, the software, which is used in

Chapter 1 and MATLAB’s Model Predictive

Control Toolbox, can be found in the appendices.

Summing up, this book is well organized. It

starts by introducing the different formulations of

predictive control and various methods used to

solve the on-line optimization problems, which

arise in predictive control with constraints, con-

tinues with nominal stability, nominal perfor-

mance and stability robustness issues for

predictive control and finishes with two case

studies. The theory of predictive control and

formulation of tuning parameters is presented and

practical issues are discussed.

There are many examples in each chapter of this

book that make the book easier understandable

for reader, particularly the novice reader. Also, at

the end of each chapter some exercises enable the

reader to assess their understanding of the

chapter’s contents. The predictive control toolbox

of MATLAB, which is common everywhere, has

been used to explain the solution procedures of

the examples and this makes the book much

better.

Most Model Predictive Controllers are formu-

lated based on the step response or transfer

function models but in this book the state space

model is used. This may make the material a little

strange for some readers. If a related CD were

included it would be easier for reader to follow the

material of the book.

Finally, the book is of interest for industrial and

process engineers who wish to apply modern

control in industry and also for researchers who

are interested in the recent progress in Predictive

Control.

MOHAMMAD H. MORADI

Faculty of EngineeringUniversity of Bu-Ali Sina

Hamadan, Iran

REFERENCES

1. Clarke DW, Mohtadi C, Tuffs PS. Generalisedpredictive control I & II. Automatica 1987; 23(2):137–160.

2. Camacho E.F, Bordons C. Model Predictive Controlin the Process Industry. Springer: London, 1995,ISBN 3-540-19924-1.

3. Camacho EF, Bordons C. Model Predictive Control.Springer: London, 1999; ISBN 3-540-76241-8.

4. Huang S, Tan KK, Lee TH. Applied PredictiveControl. Springer: London, 2002; ISBN 1-85233-338-3.

(DOI: 10.1002/acs.736)

SIGNAL PROCESSING SYSTEMS.THEORY AND DESIGN. N. Kalouptsidis,A Wiley-Interscience Publication, NewYork, 1997, pp XXI+840, price £75. ISBN0-471-11220-8.

Signals are an extremly important area that

covers many disciplines. Especially nowadays in

multimedia environments the signals like speech,

music and images need to be compressed,

encrypted, transmitted, stored, retrieved, filtered,

enhanced etc.

The book ‘Signal Processing Systems’ presents

the essentials of the signal processing (SP) that is

one of the most powerful technologies, which

have already been used in a wide range of fields

such as communications, medical imaging, remote

sensing, robotic systems etc. Each of these areas

needs deep development of digital signal proces-

sing (DSP) technology, with its own algorithms,

mathematics, and specialized techniques.

Copyright # 2003 John Wiley & Sons, Ltd. Int. J. Adapt. Control Signal Process. 2003; 17:259–263

BOOK REVIEWS262