Synchronization and control of multiagent systems

Synchronization

and Control of

Multiagent Systems

AUTOMATION AND CONTROL ENGINEERING

A Series of Reference Books and Textbooks

Series Editors

Synchronization and Control of Multiagent Systems, Dong Sun

Subspace Learning of Neural Networks, Jian Cheng Lv, Zhang Yi, and Jiliu Zhou

Reliable Control and Filtering of Linear Systems with Adaptive Mechanisms,

Guang-Hong Yang and Dan Ye

Reinforcement Learning and Dynamic Programming Using Function

Approximators, Lucian Bus¸oniu, Robert Babuška, Bart De Schutter,

and Damien Ernst

Modeling and Control of Vibration in Mechanical Systems, Chunling Du

and Lihua Xie

Analysis and Synthesis of Fuzzy Control Systems: A Model-Based Approach,

Gang Feng

Lyapunov-Based Control of Robotic Systems, Aman Behal, Warren Dixon,

Darren M. Dawson, and Bin Xian

System Modeling and Control with Resource-Oriented Petri Nets, Naiqi Wu

and MengChu Zhou

Sliding Mode Control in Electro-Mechanical Systems, Second Edition,

Vadim Utkin, Jürgen Guldner, and Jingxin Shi

Optimal Control: Weakly Coupled Systems and Applications, Zoran Gajic´,

Myo-Taeg Lim, Dobrila Skataric´, Wu-Chung Su, and Vojislav Kecman

Intelligent Systems: Modeling, Optimization, and Control, Yung C. Shin

and Chengying Xu

Optimal and Robust Estimation: With an Introduction to Stochastic Control

Theory, Second Edition, Frank L. Lewis, Lihua Xie, and Dan Popa

Feedback Control of Dynamic Bipedal Robot Locomotion, Eric R. Westervelt,

Jessy W. Grizzle, Christine Chevallereau, Jun Ho Choi, and Benjamin Morris

Intelligent Freight Transportation, edited by Petros A. Ioannou

Modeling and Control of Complex Systems, edited by Petros A. Ioannou

and Andreas Pitsillides

Wireless Ad Hoc and Sensor Networks: Protocols, Performance, and Control,

Jagannathan Sarangapani

Stochastic Hybrid Systems, edited by Christos G. Cassandras

and John Lygeros

Hard Disk Drive: Mechatronics and Control, Abdullah Al Mamun,

Guo Xiao Guo, and Chao Bi

Autonomous Mobile Robots: Sensing, Control, Decision Making

and Applications, edited by Shuzhi Sam Ge and Frank L. Lewis

FRANK L. LEWIS, Ph.D.,

Fellow IEEE, Fellow IFAC

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CRC Press is an imprint of the

Taylor & Francis Group, an informa business

Boca Raton London New York

Dong Sun

City University of Hong Kong

Kowloon, Hong Kong, People’s Republic of China

Automation and Control Engineering Series

Synchronization

and Control of

Multiagent Systems

CRC Press

Taylor & Francis Group

6000 Broken Sound Parkway NW, Suite 300

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Library of Congress Cataloging-in-Publication Data

Sun, Dong, 1967-

Synchronization and control of multiagent systems / Dong Sun.

p. cm. -- (Automation and control engineering)

Includes bibliographical references and index.

ISBN 978-1-4398-2047-6 (hardback)

1. Automatic control. 2. Multiagent systems. 3. Robotics. 4. Synchronization. I. Title.

TJ213.S7985 2010

629.8--dc22 2010035470

Visit the Taylor & Francis Web site at

http://www.taylorandfrancis.com

and the CRC Press Web site at

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Dedication

To My Family—Jiang, Sheryl, and My Parents

vii

Contents

Preface.......................................................................................................................xi

Acknowledgments.................................................................................................. xiii

About the Author .....................................................................................................xv

1Chapter Introduction ..........................................................................................1

1.1 Background................................................................................1

1.2 Synchronization.........................................................................7

1.3 Outline of the Book ................................................................. 10

1.4 Summary ................................................................................. 11

References .......................................................................................... 12

2Chapter Synchronization Strategy ................................................................... 17

2.1 Concept of Synchronization .................................................... 17

2.2 Synchronization Control Goal.................................................20

2.3 Synchronization Errors............................................................25

2.4 Summary .................................................................................27

References ..........................................................................................28

3Chapter Model-Free Synchronization Control of Multiple Motion Axes........29

3.1 Problem Statement...................................................................29

3.2 Position Synchronization Errors and Control Strategy ........... 32

3.3 Multiaxis Synchronization in Setpoint Position Control......... 33

3.4 Multiaxis Synchronization in Tracking Control......................37

3.5 Experiments.............................................................................40

3.6 Summary .................................................................................48

References .......................................................................................... 52

4Chapter Synchronized Control of Multiaxis Systems in Trajectory

Tracking.............................................................................................. 55

4.1 Synchronization Strategy of Multiagent Motions ................... 55

4.1.1 Synchronization Strategy ........................................... 57

4.2 Model-Based Cross-Coupling Synchronization Control......... 57

4.3 Adaptive Synchronization Control.......................................... 62

4.4 Case Study—Adaptive Coupling Control of Two

Working Operations in Computer Numerical Control

Integrated Machine..................................................................66

4.5 Summary ................................................................................. 71

References .......................................................................................... 71

viii Contents

5Chapter Adaptive Synchronization Control for Coordination

of Multiple Robot Manipulators.........................................................73

5.1 Introduction .............................................................................73

5.2 Motion Synchronization Strategy of Multiple

Manipulators............................................................................ 75

5.3 Adaptive Synchronization Control.......................................... 78

5.4 Case Studies.............................................................................83

5.4.1 Experiments of Coordinating Two Industrial

Manipulators ..............................................................83

5.4.2 Simulations of Coordinating Multiple

Manipulators ..............................................................89

5.5 Summary .................................................................................97

References ..........................................................................................98

6Chapter Synchronization Control for Minimization of Contouring

Errors of Computer Numerically Controlled Machine Tools .......... 101

6.1 Introduction ........................................................................... 102

6.2 Modeling of a Computer Numerical Control

Machine Tool......................................................................... 104

6.3 Contouring Errors and Synchronization Errors.................... 105

6.3.1 Contouring Errors .................................................... 105

6.3.2 Synchronization Errors ............................................ 106

6.4 Control Design....................................................................... 108

6.4.1 Controller Formulation............................................. 108

6.4.2 Stability Analysis..................................................... 110

6.5 Experiments........................................................................... 113

6.6 Summary ............................................................................... 122

References ........................................................................................125

7Chapter Synchronization Control of Parallel Robotic Manipulators............. 127

7.1 Introduction ........................................................................... 127

7.2 Modeling and Synchronization Error of Parallel

Manipulators.......................................................................... 130

7.2.1 Modeling .................................................................. 130

7.2.2 Synchronization Error.............................................. 131

7.3 Control Design....................................................................... 133

7.4 Experiments........................................................................... 138

7.5 Summary ............................................................................... 142

References ........................................................................................ 144

8Chapter A Synchronization Approach to Multirobot Formations................. 147

8.1 Introduction ........................................................................... 147

8.2 Multirobot Formation via Synchronization........................... 150

Contents ix

8.3 Control Design....................................................................... 153

8.3.1 Synchronous Formation Controller.......................... 154

8.3.2 Discussions............................................................... 158

8.3.2.1 Boundedness............................................. 158

8.3.2.2 Adaptive Control for Robustness.............. 159

8.4 Simulations............................................................................ 160

8.5 Multirobot Formation Experiments....................................... 166

8.5.1 Case 1: Triangle Formation...................................... 166

8.5.2 Case 2: Ellipse Formation ........................................ 168

8.6 Summary ............................................................................... 174

Appendix .......................................................................................... 174

References ........................................................................................ 175

Index...................................................................................................................... 179

xi

Preface

Rapid advances in sensing, computing, and communication technologies have led to

the development of autonomous systems functioning individually in uncertain envi￾ronments, opening up new challenges to understanding and developing cooperative

multiagent systems. There are many examples of multiagent systems, such as multi￾robot cooperation and multiaxis computer numerical control (CNC) machining, in

which multiple agents work cooperatively to achieve a common goal. Multiagents

are envisaged to help accomplish tasks that could not possibly be completed with

individual agents acting alone. A higher success rate and a high level of reliability in

the handling of tasks can be achieved if multiple agents are endowed with coopera￾tion capabilities.

Synchronization control provides unique advantage and opportunity to solve the

problem of multiagent coordination. Utilizing a cross-coupling concept, a synchro￾nization control framework can be developed such that all agents accomplish each

individual task while synchronizing motions among them to maintain relative kine￾matics relationships to meet the coordination requirement. The synchronization con￾trol is ideally suited to multiagent systems in performing a group task as a whole.

Some examples are multirobot assembly, multiaxis CNC machining, and formation

control of networked robots.

This book will give a detailed introduction of the cross coupling–based synchroni￾zation control approach to multiagent systems. The following chapters are included:

Chapter 1: Introduction

Chapter 2: Synchronization Strategy

Chapter 3: Model-Free Synchronization Control of Multiple Motion Axes

Chapter 4: Synchronized Control of Multiaxis Systems in Trajectory Tracking

Chapter 5: Adaptive Synchronization Control for Coordination of Multiple

Robot Manipulators

Chapter 6: Synchronization Control for Minimization of Contouring Errors of

Computer Numerically Controlled Machine Tools

Chapter 7: Synchronization Control of Parallel Robotic Manipulators

Chapter 8: A Synchronization Approach to Multirobot Formations

The key objectives of this book are as follows:

Building a connection between the multiagent coordinate task and the synchro￾nization approach. It will be shown in this book how to pose the multiagent

control problem as a synchronization control problem, permitting each

agent to be part of the coordination system while recognizing its individual

task performance capability.

Developing a theoretical framework and methodology for cooperation among

multiple agents, capable of addressing the problems of uncertain dynamic

xii Preface

models and unknown environmental disturbances. A synchronous coordi￾nation control methodology will be reported, which can guarantee both

position tracking and synchronization errors to converge to zero.

Performing application studies to demonstrate the effectiveness of the pro￾posed synchronization approach. Simulations and experiments will be

performed on various multiagent systems, such as a multiaxis CNC

machine, multiple robot manipulator, parallel manipulators, and multi￾robot in formation.

Applications of synchronization control can be found in three main areas: manu￾facturing industry, civil applications, and system biology and human health. In the

manufacturing industry, synchronization control can be widely applied to various

manufacturing automation tasks supported by surface mounting technology (SMT)

devices, CNC machines, and multirobot work cells. In civil applications, synchroni￾zation control can be used for search and rescue operations utilizing vehicles such as

helicopters or ships, and control of traffic jams in intelligent transportation systems.

Synchronization control also exhibits great potential in many applications of system

biology and human health, although this is not fully addressed in this edition of the

book. Examples include simulating and studying human interaction aspects, such as

the spread of diseases (life science), and synchronous control of multiple biological

cells in microscale or nanoscale in cell manipulation.

This book will be one of the first to systematically introduce the knowledge of

synchronization controls for multiagent systems. In addition to detailed theoretical

approaches, the book will give numerous application examples. It can be used as a

textbook for university students or a handbook for engineers to solve practical engi￾neering problems.

xiii

Acknowledgments

Several people should be mentioned for their contributions to this book. First, I

would like to thank Professor James K. Mills of the University of Toronto, Canada,

for his longtime coordination and support of this project. Second, I would like to

thank the following research associates and students at the City University of Hong

Kong and the University of Toronto for their assistance during the production of this

book: Chong Liu, Can Wang, Xiaoyin Shao, Ming Chau Tong, Chi Ming Lam, and

Lu Ren. My thanks also go to my colleague, Professor Gang Feng, who provided

valuable insights to both project implementation and the book-writing process. I am

also grateful for the help provided by the editor Li Ming Leong at Taylor & Francis

Asia Pacific.

Finally, I would like to thank Professor Sam Shuzhi Ge of the University of

Singapore and Professor Frank Lewis of the University of Texas at Arlington, for

their kind invitation and encouragement in writing this book.

xv

About the Author

Dong Sun received BSc and MSc degrees in mechatronics and biomedical engineer￾ing from Tsinghua University, China, and a PhD degree in robotics and automation

from the Chinese University of Hong Kong. From 1997 to 1999, he worked with

the University of Toronto as a postdoctoral researcher. After a short time working

as a research and development (R&D) engineer in industry in Ontario, Canada, he

joined the department of Manufacturing Engineering and Engineering Management

at the City University of Hong Kong in 2000, where he is now a full professor. He

also holds an adjunct faculty position at the University of Toronto, and is a licensed

professional engineer in Ontario, Canada.

Sun’s research interests lie in multirobot systems, robotics manipulation, motion

control, and biological processing automation. Since 2000, he has obtained more

than 30 million dollars in research grants as the principal investigator and led more

than 20 research projects toward successful completion. He has published 200 tech￾nical articles in refereed journals and conference proceedings and held four patents.

He is a fellow of the Hong Kong Institution of Engineers.

He has received numerous awards, including the Best Paper award of the 2008

Institute of Electrical and Electronics Engineers (IEEE) International Conference

on Robotics and Biomimetics, 2007 Outstanding Paper Award of IEEE Transactions

on Fuzzy System, Gold Award of 2006 Hong Kong Electronics Fair, 2004 Applied

Research Excellence Award (Certificate of Merit) of City University of Hong Kong,

and 2003 Hong Kong Award for Industry.

Sun has been actively involved in various professional activities. He serves as

an associate editor for IEEE Transactions on Robotics, technical editor for IEEE/

ASME Transactions on Mechatronics, and associate editor for the IEEE Robotics

and Automation Society Conference Board. He was chairman of the IEEE Hong

Kong section joint chapter of Robotics and Automation and Control Systems in 2007

and 2008, and led the chapter to win the best chapter award of the IEEE Robotics

and Automation Society in 2008. He has been a Program Committee and Organizing

Committee member for many international conferences, including serving as the pro￾gram chair of the 2009 IEEE International Conference on Robotics and Biomimetics,

and the general chair of the 2010 IEEE International Conference on Nano/Molecular

Medicine and Engineering.

1

1 Introduction

Abstract

This chapter will present a broad overview of multiagent systems in various

applications and a general introduction of synchronization controls, along with

basic concepts necessary to ensure understanding of the topic.

In Section 1.1, the reader will have an opportunity to learn about the back￾ground of multiagent systems and their coordination as well as future promises,

and a summary of numerous challenging problems encountered by multiagent

coordination will be presented. In Section 1.2, the reader will be exposed to

a broad range of concepts and practices of various synchronization control

technologies to multiagent systems in manufacturing automation and robotics,

where the cross-coupling control approach plays a promising role in synchro￾nizing the motions of multiple agents. Section 1.3 will present an outline of

this book. A short summary will be given in Section 1.4.

1.1 Background

Rapid advances in sensing, computing, and communication technologies have led

to the development of autonomous systems functioning individually in uncertain

environments, opening new challenges to understanding and developing cooperative

multiagent systems. Multiple agents are envisaged to help accomplish tasks that can￾not be completed with individual ones acting alone. A higher success rate and a high

level of reliability in handling those complex tasks can be achieved if multiple agents

are endowed with cooperation capabilities. Some examples of multiagent systems

include multirobot cooperation and multiaxis computer numerical control (CNC)

machining, in which multiple agents work cooperatively to achieve a common goal.

Nowadays, studies of multiagent systems and their cooperative controls have

become a popular research area with dramatically increased popularity. The idea of

multiagent working and cooperation was inspired by many examples in biology and

life science. Figure 1.1 illustrates a few biological examples of multiagent systems in

nature: ant swarming, bird flocking, and fish schooling. It is well known that multi￾agent systems, if working cooperatively under highly efficient organizations and prin￾ciples, can behave as a whole, guaranteed with fault tolerance and robust properties.

Studies of multiagent coordinative controls have attracted considerable attention

in past decades. Of these studies, two important developmental stages are gener￾ally recognized. The first stage was in the 1980s and 1990s. Many studies in this

stage focused on the coordination of multiple robot manipulators or CNC machin￾ing axes for industrial applications. The second stage started around the year 2000.

2 Synchronization and Control of Multiagent Systems

More studies in this stage focused on large scalability of the coordinated agents (i.e.,

formation and consensus controls of swarm mobile agents, in which technologies

regarding multiagent networks attract increasing attention).

The motivation behind the first-stage studies was high demand for coordination

of multiple robots in carrying out assembly tasks in both industrial and space appli￾cations. Figure 1.2 illustrates a typical example of coordinating multiple manipula￾tors in the automobile manufacturing industry. Three major coordination schemes

aiming for hybrid position and force controls have been reported in the literature.

The first scheme is the master/slave control (Arimoto et al. 1987; Luh and Zheng

1987), where one robot arm is under position control, and the others are subject

to compliant force control to maintain kinematic constraints. The second scheme

utilizes centralized control architecture (Koivo and Unseren 1991; Tarn et al. 1986;

Wen and Delgado 1992; Yoshikawa et al. 1988; Yun et al. 1997), in which robots

and the grasped payload are considered as a closed kinematic chain. This method is

designed based on a unified robot and payload dynamic model. The third scheme is a

(a) (b)

(c)

Figure 1.1 Biological examples of multiagent systems in nature: (a) ant swarming, (b) bird

flocking, and (c) fish schooling.