Table Of ContentWorld Scientific Series in Robotics and Intelligent Systems -Vol. 19
ADAPTIVE NEURAL
NETWORK CONTROL OF
ROBOTIC MANIPULATORS
S S GE, T H LEE & C J HARRIS
World Scientific
ADAPTIVE NEURAL
NETWORK CONTROL OF
ROBOTIC MANIPULATORS
WORLD SCIENTIFIC SERIES IN ROBOTICS AND INTELLIGENT SYSTEMS
Editor-in-Charge: CJ Harris (University of Southampton)
Advisor: T M Husband (University of Salford)
Published:
Vol. 1: Genetic Algorithms and Robotics — A Heuristic Strategy for Optimization
(YDavidor)
Vol. 2: Parallel Computation Systems for Robotics: Algorithms and Architectures
(Eds. A Bejczy and A Fijany)
Vol. 3: Intelligent Robotic Planning Systems (P C-Y Sheu and Q Xue)
Vol. 4: Computer Vision, Models and Inspection (A D Marshall and R R Martin)
Vol. 5: Advanced Tactile Sensing for Robotics (Ed. H R Nicholls)
Vol. 6: Intelligent Control: Aspects of Fuzzy Logic and Neural Nets (CJ Harris,
C G Moore and M Brown)
Vol. 7: Visual Servoing: Real-Time Control of Robot Manipulators Based on
Visual Sensory Feedback (Ed. K Hashimoto)
Vol. 8: Modelling and Simulation of Robot Manipulators: A Parallel Processing
Approach (A YZomaya)
Vol. 9: Advanced Guided Vehicles — Aspects of the Oxford AGV Project
(Eds. S Cameron and P Probert)
Vol. 10: Cellular Robotics and Micro Robotic Systems
(T Fukuda and T Ueyama)
Vol. 11: Recent Trends in Mobile Robots (Ed. YFZheng)
Vol. 12: Intelligent Assembly Systems (Eds. M Lee and J J Rowland)
Vol. 14: Intelligent Supervisory Control: A Qualitative Bond Graph Reasoning
Approach (H Wang and D A Linkens)
Vol. 15: Neural Adaptive Control Technology (Eds. R Zbikowski and K J Hunt)
Vol. 17: Applications of Neural Adaptive Control Technology (Eds. J Kalkkuhl,
KJ Hunt, R Zbikowski and A Dzielinski)
Vol. 20: Obstacle Avoidance in Multi-robot Systems: Experiments in Parallel
Genetic Algorithms (MA C Gill and A YZomaya)
Vol. 21: High-Level Feedback Control with Neural Networks
(YH Kim and FL Lewis)
Forthcoming:
Vol. 13: Sensor Modelling, Design and Data Processing in Confined Environments
(M D Adams)
Vol. 16: Advances in Robotics & Automation for Hazardous Environment
(Eds. P Lever and F Y Wang)
Vol. 18: Soft Computing in Systems and Control Technology
(Ed. S Tzafestas)
World Scientific Series in Robotics and Intelligent Systems -Vol. 19
ADAPTIVE NEURAL
NETWORK CONTROL OF
ROBOTIC MANIPULATORS
SSGE
National University of Singapore
THLEE
National University of Singapore
C J HARRIS
University of Southampton, UK
World Scientific
Singapore • New Jersey 'London • Hong Kong
Published by
World Scientific Publishing Co. Pte. Ltd.
P O Box 128, Farrer Road, Singapore 912805
USA office: Suite IB, 1060 Main Street, River Edge, NJ 07661
UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE
Library of Congress Cataloging-in-Publication Data
Ge, S. S. (Shuzhi S.)
Adaptive neural network control of robotic manipulators / S. S. Ge,
T. H. Lee, C. J. Harris.
p. cm. - (Series in robotics and intelligent systems ; vol. 19)
Includes bibliographical references and index.
ISBN 981023452X
1. Robots - Control systems. 2. Neural networks (Computer science)
3. Adaptive control systems. I. Lee, Tong Henry, 1958—
II. Harris, C.J. (Christopher John) III. Title. IV. Series: World
Scientific series in robotics and intelligent systems ; vol. 19.
TJ211.35.G4 1998
670.42'72-dc21 98-37310
CIP
British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.
Copyright © 1998 by World Scientific Publishing Co. Pte. Ltd.
All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means,
electronic or mechanical, including photocopying, recording or any information storage and retrieval
system now known or to be invented, without written permission from the Publisher.
For photocopying of material in this volume, please pay a copying fee through the Copyright
Clearance Center, Inc., 222 Rosewood Drive, Danvers, MA 01923, USA. In this case permission to
photocopy is not required from the publisher.
This book is printed on acid-free paper.
Printed in Singapore by UtoPrint
To
Jinlan, Ruth, Joy
and
our loved ones
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Preface
Driven by the desire for high degree of automation, fast speed operation and
good control performance from industry, extensive research work has been car
ried out on control system design. Increasingly sophisticated tools from non
linear control theory have been developed for better tracking performance.
Concurrent advances in microprocessor technology have made the implemen
tation of complex non-linear control algorithms practically feasible. Since the
early '80s, research on robotics and control theory have been mutually support
ive. The dynamics of robots are highly non-linear and dynamically coupled.
Controller design of robots has attracted considerable research interest and
posed a challenge for the robotics and control community.
Adaptive control of robotic manipulators has evolved due to the need to
obtain good performance in the presence of parametric uncertainties, payload
changes and inadequate modelling. Advanced robust control laws have been
investigated primarily based on the following techniques (a) variable structure
systems, (b) adaptive inverse dynamics and (c) model based adaptive control
by exploiting the structural properties of robots.
There is a vast amount of literature on robotics, adaptive control of robotic
systems and the applications of neural networks for control system design.
Neural network control system design of robots was motivated by the fact
that with the applications of neural networks, the exact model structure of
robots need not be known. Nonetheless, there is a lack of unification of the
above three research areas in the control of robots using neural networks and
adaptive control theory.
This book is motivated by the need to provide a framework for structured
dynamic modelling and adaptive control design of robots using neural net
works. The book, therefore, gives a brief overview of robotic dynamic mod
elling, provides the state-of-the-art of structured (neural) network modelling,
and presents stable adaptive neural network controllers for robots addressing
different issues, such as tracking control in joint space and Cartesian space,
force control, and control of flexible joint robots.
Recently, there has been considerable research interest in neural network
vn
via Preface
control for robots, and satisfactory results have been obtained in the literature.
While most of these excellent publications are for specialists and researchers
in the field, and concentrate on some of the issues associated with the prob
lems of robot control in an ''on-and-off" fashion, there is no book yet in the
literature that presents a systematic and structured approach to the modelling
and control of robots using neural networks. With this in mind, the book has
been carefully tailored to (i) give a comprehensive study of robot dynamics,
(ii) present structured network models for robots, and (iii) provide system
atic approaches for neural network based adaptive controller construction. For
completeness, rigorous proofs of the closed-loop stability properties of adap
tive neural network controllers are also provided. While extensive simulation
examples are presented to verify the effectiveness of the controllers, practical
implementation issues associated with the controllers are also appropriately
discussed.
The book starts with a brief summary of the basic mathematical concepts
of norms and stability theorems in Chapter 2. They are the tools used for
stability analysis of the controllers presented later. A detailed overview of
dynamic modelling of robots is presented in Chapter 3, which is the basis
of the structured network based modelling and adaptive controller designs in
subsequent chapters.
In Chapter 4, several different structured network based models are dis
cussed, which include dynamic neural network based, static neural network
based and parametric network based. In order to provide a general framework
for analysis, the Ge-Lee (GL) matrix and its operator are introduced to provide
a flexible tool in the analysis of the structured networks. Furthermore, owing
to the definition of GL operator, the advantage of parallel processing for neural
networks can be fully exploited for practical implementation.
In Chapter 5, a unified framework of controller design for robots is firstly
presented based on the passivity property followed by a comprehensive list of
passive parameter estimators and robust parameter estimators. Then, adaptive
neural network controllers are presented based on the structured network mod
els discussed in Chapter 4. Since the neural networks and parametric network
models discussed are structured, the resulting controllers are also given in a
well structured manner, which, consequently, lead to systematic constructions
of (neural) network based adaptive controllers.
Chapter 6 focuses on model reference adaptive control of robots based on
neural networks. One of the more established and popular methods of designing
model reference adaptive control for non-linear systems is based on the feedback
linearisation method. In this chapter, adaptive neural networks are utilised to
design model reference adaptive control for systems that are feedback linearis-
able. The second model reference adaptive control design method presented in
this chapter is to extend the already available model reference adaptive con-
Preface
IX
trol techniques for linear systems to non-linear systems using adaptive neural
networks.
The adaptive neural network controllers are further extended to flexible
joint robots in Chapter 7. With joint flexibility, many of the structural proper
ties that facilitate the control design for rigid robots are no longer applicable.
Adaptive neural network controllers for flexible joint robots are presented based
on both singular perturbation models and the non-linear feedback linearisation
approach.
In Chapter 8, we present the adaptive neural network controllers for task
space control of robots, force control of robots, and co-ordinated control of
multiple robots. Adaptive NN control in task space is first presented which
forms the basis of this chapter. Two methods of force control using adaptive
neural networks are discussed, namely, the impedance control and constrained
motion control. Interest in co-ordinated control of multiple robots stems from
the greater capability of the manipulators in carrying out more complicated
and dextrous tasks which may not be accomplished by a single manipulator. It
is an important technology for applying manipulators to industrial automation,
particularly in advanced flexible manufacturing systems.
In summary, this book covers the analysis and design of neural network
based adaptive controller, the computer simulation studies for the verification
of the performance, through to the point of actual implementation for robotic
manipulators. This book is aimed at a wide readership and is a convenient
reference for research students, academics and practising engineers.
For the creation of the book, we are very fortunate to have the kind co
operation, and helpful discussion of our colleagues, friends and co-workers.
We would like to thank Professors C. C. Hang of the National University of
Singapore, I. Postlewaite of University of Leicester, C. Canudas de Wit and
B. Brogliato of Laboratoire d'Automatique de Grenoble, Z.-L. Wang and Z.-J.
Chen of Beijing University of Aeronautics and Astronautics, B. Yao of Purdue
University, for their various contributions of the book. We are also very grateful
to Professor O. Kaynak of Bogazici University, Dr J.-X. Xu, Dr Ben M. Chen,
and Dr H. Krishnan of the National University of Singapore, Z. Lin and G.
Tao of University of Virginia, and Dr K. Y. Lou of DSO National Laboratories,
Singapore for their constructive and helpful suggestions and comments.
Last but not the least, the first author would like to thank Professor C.
B. Besant and Dr J. C. Allwright of Imperial College of Science, Technology
and Medicine for giving him the chance to pursue further study under their
supervision and for their time and efforts in supervising his PhD research on
Adaptive Non-linear Control of Robots. He is also very thankful to his current
and former graduate students, especially G. Zhu, Q. Zhao, T. Zhang, E. G.
Tan, L. C. Woon, C. P. Wong, D. L. Gu and Q. Zhu for their contributions
in simulation studies, real-time testing and among others. In particular, he
