Table Of ContentMultiagent Systems
Introduction and
Coordination Control
Magdi S Mahmoud
Distinguished Professor, Systems Engineering Department
King Fahd University of Petroleum and Minerals
Dhahran, Saudi Arabia
p,
p,
A SCIENCE PUBLISHERS BOOK
A SCIENCE PUBLISHERS BOOK
CRC Press
Taylor & Francis Group
6000 Broken Sound Parkway NW, Suite 300
Boca Raton, FL 33487-2742
© 2020 by Taylor & Francis Group, LLC
CRC Press is an imprint of Taylor & Francis Group, an Informa business
No claim to original U.S. Government works
Version Date: 20200116
International Standard Book Number-13: 978-0-367-25536-7 (Hardback)
This book contains information obtained from authentic and highly regarded sources. Reasonable efforts have been
made to publish reliable data and information, but the author and publisher cannot assume responsibility for the
validity of all materials or the consequences of their use. The authors and publishers have attempted to trace the
copyright holders of all material reproduced in this publication and apologize to copyright holders if permission to
publish in this form has not been obtained. If any copyright material has not been acknowledged please write and let
us know so we may rectify in any future reprint.
Except as permitted under U.S. Copyright Law, no part of this book may be reprinted, reproduced, transmitted,
or utilized in any form by any electronic, mechanical, or other means, now known or hereafter invented, includ-
ing photocopying, microfilming, and recording, or in any information storage or retrieval system, without written
permission from the publishers.
For permission to photocopy or use material electronically from this work, please access www.copyright.com
(http://www.copyright.com/) or contact the Copyright Clearance Center, Inc. (CCC), 222 Rosewood Drive, Danvers,
MA 01923, 978-750-8400. CCC is a not-for-profit organization that provides licenses and registration for a variety
of users. For organizations that have been granted a photocopy license by the CCC, a separate system of payment
has been arranged.
Trademark Notice: Product or corporate names may be trademarks or registered trademarks, and are used only for
identification and explanation without intent to infringe.
Visit the Taylor & Francis Web site at
http://www.taylorandfrancis.com
and the CRC Press Web site at
http://www.crcpress.com
This book is dedicated to my family. With tolerance,
patience and wonderful frame of mind, they have
encouraged and supported me for many years.
Preface
Multiagent system (MAS) is perhaps one of the most exciting and fastest growing
domains in the intelligent resource management and agent oriented technology which
deals with modeling of autonomous decision making entities. The field of multiagent
dynamic systems is an inter-disciplinary research field that has become very popular
in recent years, in parallel with the significant interest in the practical applications of
such systems in various areas, including robotics.
Recent developments have produced very encouraging results in its novel
approach to handle multi-player interactive systems. In particular, the multiagent
system approach is adapted to model, control, manage or test the operations and
management of several system applications, including multi-vehicles, microgrids
and multi-robots, where agents represent individual entities in the network. Each
participant is modeled as an autonomous participant with independent strategies
and responses to outcomes. They are able to operate autonomously and interact
proactively with their environment.
In recent publication, the problem of information consensus is addressed, where
a team of vehicles must communicate with each other in order to agree on key pieces of
information that enable them to work together in a coordinated fashion. The problem
is particularly challenging because communication channels have limited range and
experience fading and dropout. Along a parallel avenue, various topics regarding
synchronization and consensus in multiagent systems were examined. The results
demonstrated that the joint presentation of synchronization and consensus allows the
reader to learn about the similarities and differences of both concepts. Cooperative
control of multiagent dynamical systems interconnected by a communication
network topology was also studied. Using the terminology of cooperative control,
each system is endowed with its own state variable and dynamics. A fundamental
problem in multiagent dynamical systems on networks is the design of distributed
protocols that guarantee consensus or synchronization, in the sense that the states of
all the systems reach the same value.
Preface < v
In view of the available results, it turns out that research avenues in multiagent
systems offer great opportunities for further developments from theoretical, simulation
and implementations standpoints. This volume provides ‘‘system dynamics and
control perspective” of multiagent systems, with focus on mathematical modeling
of multiagent systems and paying particular attention to the agent dynamics models
available in the literature. We provide a number of problems on coordination and
control of multiagent systems which have gained significant attention recently as
well as various approaches to these problems. Looked at in this light, it has the
following objectives:
1. It gathers together the theoretical preliminaries and fundamental issues related
to multiagent systems.
2. It provides coherent results on adopting multiagent framework for critically
examining problems in smart microgrid systems.
3. It presents advanced analysis of multiagent systems under cyber-physical attacks
and develops resilient control strategies in order to guarantee safe operation.
October 2019 Magdi S Mahmoud
Acknowledgement
In writing this volume, I took the approach of referring within the text to papers
and/or books which I believe taught me some concepts, ideas and methods. I further
complemented this by adding some remarks and notes within and at the end of each
chapter to shed some light on other related results.
A particular manifestation of our life is due the ‘‘interaction” with people
in general and colleagues and friends. This is true in my technical career, where
I benefited from listening, discussing and collaborating with several colleagues.
Foremost, I would like to express my deep gratitude to Prof Yuanqing Xia, BIT,
China, who has been a good supporter, but most importantly an example of a true
friend. I owe him for his deep insight as well as for his trust and perseverance.
The process of fine tuning and producing the final draft was pursued at the
Distributed Control Research Group (DCRG), Systems Engineering Department and
special thanks must go to my colleague Dr Nezar M Alyazidi and graduate students
Mojeed O Oyedeji and Bilal J Karaki for helpful comments, superb interactions
and assistance throughout the writing of this book. The great effort by Dr Mutaz M
Hamadan was instrumental in producing the last version of the book. Portions of this
volume were developed and upgraded while offering the graduate courses SCE-612-
171, SCE-612-172, SCE-701-172, SCE-701-181, SCE-515-182 at KFUPM, Saudi
Arabia.
It is a great pleasure to acknowledge the financial funding afforded by the
deanship of scientific research (DSR) through project no. BW 181006 and for
providing a superb competitive environment and overall support of research activities
at KFUPM.
I would appreciate any comments, questions, criticisms or corrections that
readers may kindly provide to me at my emails:
[email protected] or [email protected].
Contents
Preface iv
Acknowledgement vi
Author Biography xiii
1. Introduction 1
1.1 Overview 1
1.2 Elements of Graph Theory 2
1.2.1 Basic results 3
1.2.2 Laplacian spectrum of graphs 3
1.2.3 Properties of adjacency matrix 4
1.2.4 Nonlinear stochastic dynamical systems 7
1.2.5 Complex dynamical systems 12
1.2.6 Delay effects 15
1.2.7 Sampled-data framework 16
1.3 Multiagent System Approach 17
1.3.1 Practical examples 17
1.3.2 Some relevant definitions 18
1.4 Mathematical Models for Agent Dynamics 20
1.4.1 Single integrator model 21
1.4.2 Double integrator model 21
1.4.3 Uncertain fully actuated model 22
1.4.4 Non-holonomic unicycle model 23
1.5 Coordination and Control Problems 23
1.5.1 Aggregation and social foraging 24
1.5.2 Flocking and rendezvous 25
1.5.3 Synchronization of coupled nonlinear oscillators 25
1.6 Scope and Book Layout 27
viii < Multiagent Systems: Introduction and Coordination Control
2. Theoretical Background 29
2.1 Preliminaries of Distributed Systems 29
2.1.1 Problem description 30
2.1.2 Control design scheme 32
2.1.3 Without communication delays 33
2.1.4 With communication delays 37
2.2 Networked Multiagent Systems 38
2.2.1 Consensus in networks 40
2.2.2 The f-consensus problem 41
2.2.3 Iterative consensus and Markov chains 42
2.3 Applications 42
2.3.1 Synchronization of coupled oscillators 42
2.3.2 Flocking theory 43
2.3.3 Fast consensus in small-worlds 43
2.3.4 Rendezvous in space 44
2.3.5 Distributed sensor fusion in sensor networks 44
2.3.6 Distributed formation control 44
2.4 Information Consensus 45
2.4.1 Algebraic connectivity and spectral properties 47
2.4.2 Convergence analysis for directed networks 47
2.4.3 Consensus in discrete-time 50
2.4.4 Performance of consensus algorithms 52
2.4.5 Alternative forms of consensus algorithms 54
2.4.6 Weighted-average consensus 57
2.4.7 Consensus under communication time-delays 58
2.5 Consensus in Switching Networks 59
2.6 Cooperation in Networked Control Systems 61
2.6.1 Collective dynamics of multivehicle formation 62
2.6.2 Stability of relative dynamics 63
2.7 Simulation Studies 64
2.7.1 Consensus in complex networks 64
2.7.2 Multivehicle formation control 66
2.8 Notes 67
3. Distributed Intelligence in Power Systems 69
3.1 Introduction to MAS Technology 69
3.1.1 Autonomous microgrid system 71
3.1.2 A state-space model 71
3.1.3 Heuristic dynamic programming 73
3.1.4 Discrete-time Bellman equation 74
3.1.5 Value iteration algorithm 75
3.1.6 Adaptive critics implementation 76
3.1.7 Actor-critic implementation 77
Contents < ix
3.1.8 Simulations results 78
3.1.9 Actor-critic tuning results 78
3.1.10 Robustness of the proposed controller 80
3.2 Operation in Islanded Mode 81
3.2.1 Autonomous microgrid 85
3.2.2 Primary control 86
3.2.3 Fixed gain distributed secondary control 89
3.2.4 Neural network distributed secondary control 91
3.2.5 Stage 1: Selection of training data 91
3.2.6 Stage 2: Selection of artificial neural network 94
3.2.7 Stage 3: Neural network training 95
3.2.8 Simulation results I 96
3.3 Multiagent Coordination for Distributed Energy Resources 103
3.3.1 Introduction 103
3.3.2 Advantages of MAS approach 104
3.3.3 Agent platform 105
3.3.4 Software system analysis 106
3.3.5 Distributed control system 107
3.3.6 Simulation studies I 108
3.3.7 Coordination between agents 110
3.3.8 Checking reliability 112
3.3.9 Simulation results II 113
3.4 Notes 113
4. Consensus for Heterogeneous Systems with Delays 117
4.1 Introduction 117
4.2 Multiagent Leader-Follower Consensus Problem 118
4.3 Distributed Adaptive Control Design 120
4.4 Illustrative Example 129
4.5 Tracking and Coordination Using Sensor Networks 130
4.6 Target Tracking in Sensor Networks 135
4.7 Control System Architecture 136
4.7.1 Sensor network and models 138
4.7.2 Multitarget tracking 140
4.7.3 Agent dynamics and coordination objective 143
4.8 Control System Implementation 145
4.8.1 Multisensor fusion module 145
4.8.2 Multitarget tracking and multitrack fusion modules 150
4.8.3 Multiagent coordination module 155
4.9 Experimental Results 161
4.9.1 Platform 163
4.9.2 Live demonstration 165
4.10 Notes 168
