Table Of Content

Neural 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