Table Of ContentNeural and Fuzzy Logic Control
of Drives and Power Systems
Neural and Fuzzy Logic
Control of Drives and
Power Systems
M.N. Cirstea, A. Dinu, J.G. Khor,
M. McCormick
Newnes
OXFORD AMSTERDAM BOSTON LONDON NEW YORK PARIS
SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO
Newnes
An imprint of Elsevier Science
Linacre House, Jordan Hill, Oxford OX2 8DP
225 Wildwood Avenue, Woburn, MA 01801-2041
First published 2002
Copyright © 2002, M.N. Cirstea, A. Dinu, J.G. Khor, M. McCormick. All rights reserved
The right of M.N. Cirstea, A. Dinu, J.G. Khor and M. McCormick to be identified as the
authors of this work has been asserted in accordance with the Copyright,
Designs and Patents Act 1988
No part of this publication may be reproduced in any material form (including
photocopying or storing in any medium by electronic means and whether
or not transiently or incidentally to some other use of this publication) without
the written permission of the copyright holder except in accordance with the
provisions of the Copyright, Designs and Patents Act 1988 or under the terms of
a licence issued by the Copyright Licensing Agency Ltd, 90 Tottenham Court Road,
London, England W1T 4LP. Applications for the copyright holder’s written
permission to reproduce any part of this publication should be addressed
to the publisher
British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library
ISBN 0 7506 55585
For information on all Newnes publications
visit our website at www.newnespress.com
Typeset at Replika Press Pvt Ltd, Delhi 110 040, India
Printed and bound in Great Britain
...................................................P..r.eface
Control systems ....................................... 1
Control theory: historical review ............. 1
Introduction to control systems............... 2
Control systems for a. c. drives.............. 5
Modern control systems design using
......................C..A...D.. .t..e..c..h..n..iques
Electronic design automatio.n.. .(. EDA)
Application specific integrated circuit (
ASIC) basics........................................... 12
Field programmable gate arrays (
FPGAs)................................................... 14
ASICs for power systems and drives ..... 16
Electric motors and power. .s..y..s. tems
..........................E..l.e..c..t.r.i.c. .m.. otors
Power systems....................................... 19
Pulse width modulation .......................... 22
The space vector in electrical systems... 26
Induction motor control........................... 28
Synchronous generators control ............ 51
Elements.. .o..f.. .n..e..u..r.a..l. .c..o. ntrol
..........................N...e..u..r.o..n..e.. types
Artificial neural networks architectures... 59
Training algorithms................................. 61
Control applications of ANNs ................. 69
Neural network implementation.............. 71
Neural FPGA .i.m...p..l.e..m....e..n..tation
Neural networks design and
imp..l.e..m...e..n..t.a..t.i.o..n.. .s..t.rategy
Universal programs (cid:210) FFANN
hardware implementation....................... 95
Hardware implementation complexity
analysis .................................................. 98
Fuzzy .l.o..g..i.c.. .f.u...n..d..a..m...e.ntals
....................H...i.s..t.o..r.i.c.a..l. .r.eview
Fuzzy sets and fuzzy logic ..................... 114
Types of membership functions ............. 116
Linguistic variables................................. 117
Fuzzy logic operators ............................. 117
Fuzzy control systems............................ 118
Fuzzy logic in power and control
applications ............................................ 121
........V...H..D...L.. .f.u...n..d..a..m...e.ntals
....................................I.n..t.r.o..d..uction
VHDL design units.................................. 126
Libraries, visibility and state system in
VHDL...................................................... 131
Sequential statements............................ 135
Concurrent statements........................... 141
Functions and procedures...................... 146
Advanced features in VHDL................... 151
Summary................................................ 154
Neural current and speed control of
..................i.n...d..u..c..t.i.o..n.. .m...otors
The induction motor equivalen..t. .circuit
The current control algorithm ................. 161
The new sensorless motor control
strategy................................................... 183
Induction motor controller VHDL
design..................................................... 199
FPGA controller experimental results..... 227
Fuzzy logic control of a synchronous
................................g..e..n..e..r..a..t.or set
.S..y..s..t.e..m... .r.e..p..r.e..s..e..n.tation
VHDL modelling ..................................... 248
FPGA implementation ............................ 270
System assembly and experimental
tests........................................................ 285
Conclusions............................................ 292
.......................................F..i.n..a..l. .notes
......................................R...e..f.e..r.e. nces
....................................A...p..p..e..n..dices
Appendix A - C++ code for ANN
........................i.m...p..l.e..m...e..n.tation
Appendix B - C++ Programs for PWM
generation .............................................. 333
Appendix C - Subnetworks VHDL
models.................................................... 341
Appendix D - VHDL model of sine
wave ROM.............................................. 355
Appendix E - VHDL code for
simulation............................................... 357
Appendix F - VHDL code for synthesis .. 374
Appendix G - PWM controllers............... 389
Index
Preface
The idea of writing this book arose from the need to investigate the main principles of
modern power electronic control strategies, using fuzzy logic and neural networks, for
research and teaching. Primarily, the book aims to be a quick learning guide for
postgraduate/undergraduate students or design engineers interested in learning the
fundamentals of modern control of drives and power systems in conjunction with the
powerful design methodology based on VHDL.
At the same time, the book is structured to address the more complex needs of
professional designers, using VHDL for neural and fuzzy logic systems design, by
including comprehensive design examples. This facilitates the understanding of hardware
description language applications and provides a practical approach to the development
of advanced controllers for power electronics.
The first section of the book contains a brief review of control strategies for electric
drives/power systems and a summary description of neural networks, fuzzy logic, electronic
design automation (EDA) techniques, ASICs/FPGAs and VHDL. The aspects covered
allow a basic understanding of the main principles of modern control. The second
section contains two comprehensive case studies. The first deals with neural current and
speed control of induction motor drives, whereas the second presents the environmentally
friendly fuzzy logic control of a diesel-driven stand-alone synchronous generator set.
Both control strategies were implemented in Xilinx FPGAs and comprehensively tested
by simulation and experimental measurements.
This book brings together the complex features of control strategies, EDA, neural
networks, fuzzy logic, electric machines and drives, power systems and VHDL and
forms a basic guide for the understanding of the fundamental principles of modern
power electronic control systems design. To be expert in the design of advanced digital
controllers for drives and power systems, extra reading is strongly recommended and
comprehensive material is referenced in the bibliographical section. The book includes
a number of recent research results from work carried out by the authors, who are
members of the electronic control and drives research group at De Montfort University,
Leicester, UK.
The facilities provided by the university and the support of NEWAGE AVK SEG,
Stamford, UK, a major international manufacturer of electric generators, are gratefully
acknowledged.
Dr Marcian N. Cirstea
Dr Andrei Dinu
Dr Jeen G. Khor
Prof. Malcolm McCormick
1
Control systems
1.1 Control theory: historical review
The function of a control mechanism is to maintain certain essential properties of a
system at a desired value under perturbations. Historical control systems which are
simple but effective have been employed in water regulation and control of liquid level
in wine vessels for centuries. Some of these concepts are still used today, for example
the float system in the water tank of the toilet flush. However, modern control systems
used in today’s industry are much more complex and owe their beginnings to the
development of control theory. The earliest significant work in modern automatic control
can be traced to James Watt’s design of the fly-ball governor (1788) for the speed
control of a steam engine. In 1868, Maxwell [170] presented the first mathematical
analysis of feedback control. It was during this time that systematic studies into control
systems and feedback dynamics began. One significant development was the well-
known Routh’s stability criterion (1877) which won E.J. Routh the Adam’s Prize.
The early twentieth century saw the beginning of what is now known as classical
control theory. Minorsky’s work (1922) on the determination of stability from the
differential equation describing the system (characteristic equation) and Nyquist’s
development (1932) of a graphical procedure for determining stability (frequency response)
substantially contributed to the study of control theory. In 1934, Hazen [111] introduced
the term ‘servomechanism’ to describe position control systems in his attempt to develop
a generalised theory of servomechanisms. Two years later, the development of the
proportional integral derivative (PID) controller was described by Callender et al.
(1936). Control theory, like many branches of engineering, underwent significant
development during World War II. Based on Nyquist’s work, H.W. Bode introduced a
method for feedback amplifier design, now known as the Bode plot (1945). By 1948, the
root locus method of design and stability analysis was developed by W.R. Evans [93].
With the introduction of digital computers in the 1960s, the use of frequency response
and characteristic equations began to give way to ordinary differential equations (ODEs),
which worked well with computers. This led to the birth of modern control theory.
While the term classical control theory is used to describe the design methods of
Bode, Nyquist, Minorsky and similar workers, modern control theory relies on ODE
design methods that are more suitable for computer aided engineering, for example the
state space approach. Both these branches of control theory rely on mathematical
representation of the control plant from which to derive its performance. To address the
issues of non-linearities and time-variant parameters in plant models, control strategies
