Adaptive Backstepping Consensus Control for Nonlinear Multi-agent Systems : Command Filtered Backstepping

Adaptive Backstepping Consensus Control for Nonlinear Multi-Agent Systems: Command Filtered Backstepping offers a new design solution for students, researchers, and engineers working on distributed cooperative control problems for nonlinear multi-agent systems. The book is structured around six key topics, focusing on command filtered backstepping-based distributed adaptive consensus control. By combining command filtered backstepping techniques with adaptive control, fuzzy logic systems, neural networks, and other control approaches, the book investigates and proposes control schemes for the consensus control problem of nonlinear multi-agent systems. Readers will gain a comprehensive understanding of consensus control based on adaptive command filtered backstepping technology.

• Provides a framework for dealing with various nonlinearities in multi-agent cooperative control problems
• Proposes the corresponding intelligent adaptive control schemes to compensate for uncertainties and disturbances
• Includes applications to demonstrate the advantages of cooperative adaptive command filtered backstepping control approaches

Lin Zhao received a B.Sc. degree in Mathematics and Applied Mathematics from Qingdao University, Qingdao, China, in 2008, and a M.Sc. degree in Operational Research and Cybernetics from the Ocean University of China, Qingdao, in 2011. Zhao earned a Ph.D. degree in Applied Mathematics from Beihang University, Beijing, China, in 2016. He is currently a Professor with the School of Automation, Qingdao University. His current research interests include distributed control of multiagent systems, finite-time control, and robot control systems. Dr. Zhao was the recipient of the Shandong Province Taishan Scholar Special Project Fund and the Shandong Province Fund for Outstanding Young Scholars.

Jinpeng Yu received a B.Sc. degree in Automation from Qingdao University, Qingdao, China, in 2002, and an M.Sc. degree in System Engineering from Shandong University, Jinan, China, in 2006. Yu went on to obtain a Ph.D. degree in System Theory from the Institute of Complexity Science, Qingdao University, in 2011. He is currently a Professor with the School of Automation, Qingdao University. His research interests include electrical energy conversion and motor control, applied nonlinear control, and intelligent systems. Dr. Yu was the recipient of the Shandong Province Taishan Scholar Special Project Fund and Shandong Province Fund for Outstanding Young Scholars. He has been an Associate Editor of several reputable journals.