Zhengtao Ding - Böcker

Cooperative Control of Multi-Agent Systems: An Optimal and Robust Perspective reports and encourages technology transfer in the field of cooperative control of multi-agent systems. The book deals with UGVs, UAVs, UUVs and spacecraft, and more. It presents an extended exposition of the authors' recent work on all aspects of multi-agent technology. Modelling and cooperative control of multi-agent systems are topics of great interest, across both academia (research and education) and industry (for real applications and end-users). Graduate students and researchers from a wide spectrum of specialties in electrical, mechanical or aerospace engineering fields will use this book as a key resource.

Helps shape the reader's understanding of optimal and robust cooperative control design techniques for multi-agent systems Presents new theoretical control challenges and investigates unresolved/open problems Explores future research trends in multi-agent systems Offers a certain amount of analytical mathematics, practical numerical procedures, and actual implementations of some proposed approaches

This book presents a concise introduction to the latest advances in robust cooperative control design for multi-agent systems with input delay and external disturbances, especially from a prediction and observation perspective. The volume covers a wide range of applications, such as the trajectory tracking of quadrotors, formation flying of multiple unmanned aerial vehicles (UAVs) and fixed-time formation of ground vehicles.Robust cooperative control means that multi-agent systems are able to achieve specified control tasks while remaining robust in the face of both parametric and nonparametric model uncertainties. In addition, the authors cover a wide range of key issues in cooperative control, such as communication and input delays, parametric model uncertainties and external disturbances. Moving beyond the scope of existing works, a systematic prediction and observation approach to designing robust cooperative control laws is presented.About the AuthorsChunyan Wang is an Associate Professor in the School of Aerospace Engineering at Beijing Institute of Technology, China.Zongyu Zuo is a full Professor with the School of Automation Science and Electrical Engineering, Beihang University, China.Jianan Wang is an Associate Professor in the School of Aerospace Engineering at Beijing Institute of Technology, China.Zhengtao Ding is a Professor in the Department of Electrical and Electronic Engineering at University of Manchester, U.K.

This book presents a concise introduction to the latest advances in robust cooperative control design for multi-agent systems with input delay and external disturbances, especially from a prediction and observation perspective. The volume covers a wide range of applications, such as the trajectory tracking of quadrotors, formation flying of multiple unmanned aerial vehicles (UAVs) and fixed-time formation of ground vehicles.Robust cooperative control means that multi-agent systems are able to achieve specified control tasks while remaining robust in the face of both parametric and nonparametric model uncertainties. In addition, the authors cover a wide range of key issues in cooperative control, such as communication and input delays, parametric model uncertainties and external disturbances. Moving beyond the scope of existing works, a systematic prediction and observation approach to designing robust cooperative control laws is presented.About the AuthorsChunyan Wang is an Associate Professor in the School of Aerospace Engineering at Beijing Institute of Technology, China.Zongyu Zuo is a full Professor with the School of Automation Science and Electrical Engineering, Beihang University, China.Jianan Wang is an Associate Professor in the School of Aerospace Engineering at Beijing Institute of Technology, China.Zhengtao Ding is a Professor in the Department of Electrical and Electronic Engineering at University of Manchester, U.K.

Distributed Optimization and Learning: A Control-Theoretic Perspective illustrates the underlying principles of distributed optimization and learning. The book presents a systematic and self-contained description of distributed optimization and learning algorithms from a control-theoretic perspective. It focuses on exploring control-theoretic approaches and how those approaches can be utilized to solve distributed optimization and learning problems over network-connected, multi-agent systems. As there are strong links between optimization and learning, this book provides a unified platform for understanding distributed optimization and learning algorithms for different purposes.

Provides a series of the latest results, including but not limited to, distributed cooperative and competitive optimization, machine learning, and optimal resource allocationPresents the most recent advances in theory and applications of distributed optimization and machine learning, including insightful connections to traditional control techniquesOffers numerical and simulation results in each chapter in order to reflect engineering practice and demonstrate the main focus of developed analysis and synthesis approaches