Springer Distributed Average Tracking in Multi-agent Systems

This book presents a systematic study of an emerging field in the development of multi-agent systems. In a wide spectrum of applications, it is now common to see that multiple agents work cooperatively to accomplish a complex task. The book assists the implementation of such applications by promoting the ability of multi-agent systems to track ― using local communication only ― the mean value of signals of interest, even when these change rapidly with time and when no individual agent has direct access to the average signal across the whole team; for example, when a better estimation/control performance of multi-robot systems has to be guaranteed, it is desirable for each robot to compute or track the averaged changing measurements of all the robots at any time by communicating with only local neighboring robots. The book covers three factors in successful distributed average tracking: algorithm design via nonsmooth and extended PI control; distributed average tracking for double-integrator, general-linear, Euler–Lagrange, and input-saturated dynamics; and applications in dynamic region-following formation control and distributed convex optimization.  The book presents both the theory and applications in a general but self-contained manner, making it easy to follow for newcomers to the topic. The content presented fosters research advances in distributed average tracking and inspires future research directions in the field in academia and industry. From the Back Cover This book presents a systematic study of an emerging field in the development of multi-agent systems. In a wide spectrum of applications, it is now common to see that multiple agents work cooperatively to accomplish a complex task. The book assists the implementation of such applications by promoting the ability of multi-agent systems to track ― using local communication only ― the mean value of signals of interest, even when these change rapidly with time and when no individual agent has direct access to the average signal across the whole team; for example, when a better estimation/control performance of multi-robot systems has to be guaranteed, it is desirable for each robot to compute or track the averaged changing measurements of all the robots at any time by communicating with only local neighboring robots. The book covers three factors in successful distributed average tracking: algorithm design via nonsmooth and extended PI control; distributed average tracking for double-integrator, general-linear, Euler–Lagrange, and input-saturated dynamics; and applications in dynamic region-following formation control and distributed convex optimization.   The book presents both the theory and applications in a general but self-contained manner, making it easy to follow for newcomers to the topic. The content presented fosters research advances in distributed average tracking and inspires future research directions in the field in academia and industry. About the Author Fei Chen received the Ph.D. degree in control theory and control engineering from Nankai University, China, in 2009. He is presently a Professor with the State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, China and School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao, China. He serves as the deputy director of Qinhuangdao Key Laboratory of Simulation and Control of Intelligent Transportation Systems. Prior to joining Northeastern University, he was with the Department of Automation, Xiamen University, Xiamen, China and the Department of Computer and Electrical Engineering, Utah State University, Logan, UT. He also held several visiting positions at University of California, Riverside, and City University of Hong Kong. His research interests include multi-agent systems, nonsmooth control, cooperative control, distributed optimization, and complex networks. Dr.