任伟《Distributed Consensus in Multi-vehicle Cooperative Control》

【实例简介】
分布式控制大牛任伟老师的专著 英文版 非常适合初学者学习
Wei ren and randal w. beard Distributed Consensus in Multi-vehicle Cooperative Control Theory and Applications ②s pringer Wei ren phD Randal w. beard PhD Department of Electrical and Computer Department of Electrical and Computer Engineering Engineering Utah State University Brigham Young University Logan,UT84322-4120 Provo, UT84602 USA USA ISBN978-1-84800-0148 e-ISBN978-1-84800-015-5 DOI10.10071978-1-84800-015-5 Communications and Control Engineering Series ISSN 0178-5354 British Library Cataloguing in Publication Data Ren, Wei Distributed consensus in multi-vehicle cooperative control theory and applications. - Communications and control engineering 1. Digital control systems 2. Automatic control Mathematics 3 Algorithms I. Title Il. beard, Randal w. 62989015181 ISBN-13:9781848000148 Library of Congress Control Number: 2007934265 C 2008 Springer-Verlag London Limited Apart from any fair dealing for the purposes of research or private study, or criticism or review,as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers, or in the case of reprographic reproduction in accordance with the terms of licences issued by the Copyright Licensing Agency. Enquiries concerning reproduction outside those terms should be sent to the ublishers The use of registered names, trademarks, etc in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant laws and regulations and therefore free ge ne al use The publisher makes no representation, express or implied, with regard to the accuracy of the information contained in this book and cannot accept any legal responsibility or liability for any errors or omissions that may be made Cover design: LE-TEX Jelonek, Schmidt Vockler GbR, Leipzig, Germany Printed on acid-free paper 987654321 nger. com This work is dedicated to My wife, Fei Cheng, and My parents, Hongyi Ren and Liying Wang Wei rc My wife, Andre Randal w. beard Preface Recent advances in miniaturizing of computing, communication, sensing, and actuation have made it feasible to envision large numbers of autonomous ve- hicles(air, ground, and water) working cooperatively to accomplish an ob jective. Cooperative control of multiple vehicle systems has potential impact in numerous civilian, homeland security, and military applications. Potential civilian applications include monitoring forest fires, oil fields, pipelines, and tracking wildlife. Potential homeland security applications include border pa trol and monitoring the perimeter of nuclear power plants. For the military applications include surveillance, reconnaissance, and battle damage assess ment. However, for all of these applications, communication bandwidth and power constraints will preclude centralized command and control This book addresses the problem of information consensus, where a team of vehicles must communicate with its neighbors 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 lim ited range and experience fading and dropout. The study of information flow and sharing among multiple vehicles in a group plays an important role in understanding the coordinated movements of these vehicles. As a result, a critical problem for cooperative control is to design appropriate distributed algorithms such that the group of vehicles can reach consensus on the shared information in the presence of limited and unreliable information exchange and dynamically changing interaction topologies Our interest in distributed consensus algorithms and their applications was motivated by our research efforts in cooperative control of multiple vehi cle systems and, in particular, teams of unmanned air vehicles. Air vehicles are constantly moving and consequently their ability to communicate is dynami cally changing. In addition, in current military scenarios involving unmanned air vehicles, large assets like the Predator may have two-way communic tion capabilities, but micro air vehicles may have only the ability to receive commands. Therefore, we were motivated to study distributed coordination Preface problems where the communication network is noisy, limited, time-varying and possibly unidirectional Of course, coming into consensus, or agreement, is not the only issue. Each member of the team must act to achieve the team objective using the best available information. The interplay between communications/consensus and control introduces significant challenges that are only beginning to be un derstood. In much of the current research on cooperative control, either the consensus problem is studied in the absence of an application, or the coop- erative control problem is studied under the assumption of full and reliable communication Our objective in writing this research monograph is to summarize our work in cooperative control using distributed consensus algorithms. The monograph is roughly divided into two parts. In the first half of the book( Chapters 1-7) we describe theoretical results on distributed consensus algorithms where the dynamics of the information state evolve according to first- and second-order dynamics and according to rigid body attitude dynamics. The consensus algo rithms require only neighbor-to-neighbor interaction which minimizes power consumption, increases stealth, and improves the scalability and robustness of the team. The second half of the book( Chapters 8-14)describes our attempts to apply the theory to a variety of applications in cooperative control, includ ing formation keeping for wheeled mobile robots and spacecraft and cooper ative perimeter tracking and timing for a team of unmanned air vehicles. We maintainawebsitehttp://www.engineering.usu.edu/ece/faculty/wren/ book/consensus at which can be found sample simulation and experimental videos and other useful materials associated with the book The results in this book and particularly the results in Chapters 8-14 would not have been possible without the efforts and support of our col- leagues and students. In particular, we are indebted to Professor Tim Mclain at Brigham Young University for his leadership in the area of cooperative control for unmanned air vehicles and for countless discussions on consensus and other applications of cooperative control. We are also indebted to Profes sor Ella atkins at the University of Michigan and Professors YangQuan Chen and Mac McKee at Utah State University for many fruitful discussions on research ideas. We also acknowledge the efforts of Nathan Sorensen, yongcan Cao, haiyang Chao, William Bourgeous, and larry ballard at Utah State Uni- versity, and Derek Kingston, Jonathan Lawton, Brett Young, David Casbeer Ryan Holt, Derek Nelson, Blake Barber, Stephen griffiths, David Johansen and Andrew Eldridge at Brigham Young University. We are thankful to our editor Oliver Jackson for his interest in our project and his professionalism In addition, we acknowledge IEEE, John Wiley sons, Elsevier, AIAA, and Taylor francis for granting us the permission to reuse materials from our publications copyrighted by these publishers in this book. The last section of each chapter gives a detailed list of the references used in the chapter. Finally, we gratefully acknowledge the support of our research on consensus algorithms and cooperative control by the Utah Water Research Laboratory and Com- Preface munity /University Research Initiative as well as National Science Foundation under Information Technology Research Grant CCR-0313056, NASA under STTR Contract No. NNA04AA19C. Air Force Office of Scientific Research under award no.F49550-04-0209.F49620-01-1-0091.andF49620-02C-0094 and Defense Agency Research Projects Agency under Grant NBCH1020013 Utah State University, Logan, Utah Wei ren Brigham Young University, Provo, Utah Randal w. beard September 2007 Contents Part I Overview of Consensus Algorithms in Cooperative Control 1 Overview of Consensus Algorithms in Cooperative Contro 1.1 Introduction 1.2 Literature Review: Consensus algorithms 1.2.1 Fundamental Consensus Algorithms 1.2.2 Convergence analysis of Consensus algorithms 336795 1.2.3 Synthesis and Extensions of Consensus algorithms 1.2.4 Design of Coordination Strategies via Consensus Algorithms 1.3 Monograph Overview 21 1.4 Notes 22 Part II Consensus Algorithms for Single-integrator Dynamics 2 Consensus Algorithms for Single-integrator dynamics 25 2.1 Fundamental algorithms 25 2.2 Consensus Under Fixed Interaction Topologies 2.2.1 Consensus Using a Continuous-time AlgorithIn 28 2.2.2 Consensus Using a Discrete-time Algorithm 38 2.3 Consensus Under Dynamically Changing Interaction Topologies 42 2.3.1 Consensus Using a Continuous-time Algorithm........ 45 2.3.2 Consensus Using a Discrete-time algorithm 49 2.3.3 Simulation Results 50 2. 4 Notes 3 Consensus Tracking with a Reference State 55 3.1 Problem Statement 55 3.2 Constant Consensus Reference State 56 3.3 Time-varying Consensus Reference State 58 XI Contents 3.3.1 Fundamental Consensus Tracking algorithm 61 3.3.2 Consensus Tracking Algorithm with Bounded Control nputs 66 3.3.3 Information Feedback to the Consensus Reference State 68 3.4 Extension to relative State deviations 71 3. 5 Note Part III Consensus Algorithms for Double-integrator Dynamics 4 Consensus Algorithms for Double-integrator Dynamics.. 77 4.1 Consensus Algorithm 77 4.1.1 Convergence Analysis Under Fixed Interaction Topologie ..79 4.1.2 Convergence Analysis Under Switching Interaction Topologie 91 4.2 Consensus with Bounded Control Inputs 96 4.3 Consensus Without Relative State Derivative Measurements. 100 4.4 Notes 103 5 Extensions to a reference model 105 5.1 Problem Statement 105 5.2 Consensus with a reference for Information State Derivatives. 106 5.2.1 Consensus with Coupling Between Neighbors Information State Derivatives 106 5.2.2 Consensus Without Coupling Between Neighbors Information State Derivatives 109 5.3 Consensus with References for Information States and their Derivatives 5.3.1 Full access to the reference model 112 5.3.2 Leader-following Strategy 113 5.3. 3 General Case ,114 5.4 Notes 118 Part IV Consensus Algorithms for Rigid Body Attitude Dynamics 6 Consensus Algorithms for Rigid Body Attitude Dynamics. 123 6.1 Problem Statement 123 6.2 Attitude Consensus with Zero Final Angular Velocities 124 6.3 Attitude Consensus Without absolute and Relative Angular Ⅴ elocity Measurement 128 6.4 Attitude consensus with Nonzero final angular velocities..131 6.5 Simulation results .132 6.6 Note 134 【实例截图】
【核心代码】