High Performance Control.PDF

High Performance Control

T. T. Tay 1

I. M. Y. Mareels 2

J. B. Moore 3

1997

1. Department of Electrical Engineering, National University of Singapore, Sin-

gapore.

2. Department of Electrical and Electronic Engineering, University of Melbourne,

Australia.

3. Department of Systems Engineering, Research School of Information Sciences

and Engineering, Australian National University, Australia.

Preface

The engineering objective of high performance control using the tools of optimal

control theory, robust control theory, and adaptive control theory is more achiev-

able now than ever before, and the need has never been greater. Of course, when

we use the term high performance control we are thinking of achieving this in

the real world with all its complexity, uncertainty and variability. Since we do not

expect to always achieve our desires, a more complete title for this book could be

“Towards High Performance Control”.

To illustrate our task, consider as an example a disk drive tracking system for a

portable computer. The better the controller performance in the presence of eccen-

tricity uncertainties and external disturbances, such as vibrations when operated

in a moving vehicle, the more tracks can be used on the disk and the more memory

it has. Many systems today are control system limited and the quest is for high

performance in the real world.

In our other texts Anderson and Moore (1989), Anderson and Moore (1979),

Elliott, Aggoun and Moore (1994), Helmke and Moore (1994) and Mareels and

Polderman (1996), the emphasis has been on optimization techniques, optimal es-

timation and control, and adaptive control as separate tools. Of course, robustness

issues are addressed in these separate approaches to system design, but the task

of blending optimal control and adaptive control in such a way that the strengths

of each is exploited to cover the weakness of the other seems to us the only way

to achieve high performance control in uncertain and noisy environments.

The concepts upon which we build were ﬁrst tested by one of us, John Moore,

on high order NASA ﬂexible wing aircraft models with ﬂutter mode uncertainties.

This was at Boeing Commercial Airplane Company in the late 1970s, working

with Dagﬁnn Gangsaas. The engineering intuition seemed to work surprisingly

well and indeed 180 phase margins at high gains was achieved, but there was

a shortfall in supporting theory. The ﬁrst global convergence results of the late

1970s for adaptive control schemes were based on least squares identiﬁcation.

These were harnessed to design adaptive loops and were used in conjunction with

Preface

linear quadratic optimal control with frequency shaping to achieve robustness to

ﬂutter phase uncertainty. However, the blending of those methodologies in itself

lacked theoretical support at the time, and it was not clear how to proceed to

systematic designs with guaranteed stability and performance properties.

A study leave at Cambridge University working with Keith Glover allowed

time for contemplation and reading the current literature. An interpretation of the

Youla-Kucera result on the class of all stabilizing controllers by John Doyle gave

a clue. Doyle had characterized the class of stabilizing controllers in terms of a

stable ﬁlter appended to a standard linear quadratic Gaussian LQG controller de-

sign. But this was exactly where our adaptive ﬁlters were placed in the designs

we developed at Boeing. Could we improve our designs and build a complete

theory now? A graduate student Teng Tiow Tay set to work. Just as the ﬁrst simu-

lation studies were highly successful, so the ﬁrst new theories and new algorithms

seemed very powerful. Tay had also initiated studies for nonlinear plants, conve-

niently characterizing the class of all stabilizing controllers for such plants.

At this time we had to contain ourselves not to start writing a book right

away. We decided to wait until others could ﬂesh out our approach. Iven Mareels

and his PhD student Zhi Wang set to work using averaging theory, and Roberto

Horowitz and his PhD student James McCormick worked applications to disk

drives. Meanwhile, work on Boeing aircraft models proceeded with more con-

servative objectives than those of a decade earlier. No aircraft engineer will trust

an adaptive scheme that can take over where off-line designs are working well.

Weiyong Yan worked on more aircraft models and developed nested-loop or it-

erated designs based on a sequence of identiﬁcation and control exercises. Also

Andrew Paice and Laurence Irlicht worked on nonlinear factorization theory and

functional learning versions of the results. Other colleagues Brian Anderson and

Robert Bitmead and their coworkers Michel Gevers and Robert Kosut and their

PhD students have been extending and reﬁning such design approaches. Also,

back in Singapore, Tay has been applying the various techniques to problems

arising in the context of the disk drive and process control industries.

Now is the time for this book to come together. Our objective is to present the

practice and theory of high performance control for real world environments. We

proceed through the door of our research and applications. Our approach special-

izes to standard techniques, yet gives conﬁdence to go beyond these. The idea is

to use prior information as much as possible, and on-line information where this is

helpful. The aim is to achieve the performance objectives in the presence of vari-

ations, uncertainties and disturbances. Together the off-line and on-line approach

allows high performance to be achieved in realistic environments.

This work is written for graduate students with some undergraduate back-

ground in linear algebra, probability theory, linear dynamical systems, and prefer-

ably some background in control theory. However, the book is complete in itself,

including appropriate appendices in the background areas. It should appeal to

those wanting to take only one or two graduate level semester courses in control

and wishing to be exposed to key ideas in optimal and adaptive control. Yet stu-

dents having done some traditional graduate courses in control theory should ﬁnd

Preface

vii

that the work complements and extends their capabilities. Likewise control engi-

neers in industry may ﬁnd that this text goes beyond their background knowledge

and that it will help them to be successful in their real world controller designs.

Acknowledgements

This work was partially supported by grants from Boeing Commercial Airplane

Company, and the Cooperative Research Centre for Robust and Adaptive Sys-

tems. We wish to acknowledge the typesetting and typing support of James Ash-

ton and Marita Rendina, and proof reading support of PhD students Andrew Lim

and Jason Ford.

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