Distributed Average Tracking in Multi-agent Systems

Chen, Fei; Ren, Wei

ISBN 10: 3030395383 / ISBN 13: 9783030395384

Verlag: Springer, 2021

Bestandsnummer des Verkäufers 43169505-n

Inhaltsangabe:

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.

Über die Autorin bzw. den Autor:

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. Chen was a recipient of 2014 Outstanding Reviewer for IEEE Transactions on Control of Network Systems. He is currently an Associate Editor for IEEE ACCESS. He is a senior member of the IEEE.

 

Wei Ren received the B.S. degree in electrical engineering from Hohai University, China, in 1997, the M.S. degree in mechatronics from Tongji University, China, in 2000, and the Ph.D. degree in electrical engineering from Brigham Young University, Provo, UT, in 2004. He is currently a Professor with the Department of Electrical and Computer Engineering, University of California, Riverside. Prior to joining UC Riverside, he was a faculty member at Utah State University and a postdoctoral research associate at the University of Maryland, College Park. His research focuses on distributed control of multi-agent systems. He is an author of two books Distributed Coordination of Multi-agent Networks (Springer, 2011) and Distributed Consensus in Multi-vehicle Cooperative Control (Springer,2008). Dr. Ren was the recipient of the IEEE Control Systems Society Antonio Ruberti Young Researcher Prize in 2017 and a National Science Foundation CAREER Award in 2008. He is currently an Associate Editor for Automatica and IEEE Transactions on Automatic Control. He is an IEEE Fellow.

Bibliografische Details

Titel: Distributed Average Tracking in Multi-agent ...
Verlag: Springer
Erscheinungsdatum: 2021
Einband: Softcover
Zustand: New