Iterative Learning Control for Multi-agent Systems Coordination (IEEE Press) - Hardcover

Yang, Shiping; Xu, Jian-Xin; Li, Xuefang; Shen, Dong

Hardcover

ISBN 10: 1119189047 ISBN 13: 9781119189046

Publisher: Wiley-IEEE Press, 2017

Synopsis

A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, showcasing recent advances and industrially relevant applications

• Explores the synergy between the important topics of iterative learning control (ILC) and multi-agent systems (MAS)
• Concisely summarizes recent advances and significant applications in ILC methods for power grids, sensor networks and control processes
• Covers basic theory, rigorous mathematics as well as engineering practice

About this title

About the Author

Shiping Yang, Jian-Xin Xu, and Xuefang Li
National University of Singapore

Dong Shen
Beijing University of Chemical Technology, P.R. China

From the Back Cover

A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, this book showcases recent advances and industrially relevant applications. Readers are first given a comprehensive overview of the intersection between ILC and MAS, then introduced to a range of topics that include both basic and advanced theoretical discussions, rigorous mathematics, engineering practice, and both linear and nonlinear systems. Through systematic discussion of network theory and intelligent control, the authors explore future research possibilities, develop new tools, and provide numerous applications such as power grids, communication and sensor networks, intelligent transportation systems, and formation control. Readers will gain a roadmap of the latest advances in the fields and can use their newfound knowledge to design their own algorithms.

• Explores the synergy between the important topics of iterative learning control (ILC) and multi-agent systems (MAS)
• Concisely summarizes recent advances and significant applications in ILC methods for power grids, sensor networks, and control processes
• Covers basic theory and rigorous mathematics as well as engineering practice

Written by experienced researchers, Iterative Learning Control for Multi-agent Systems Coordination will appeal to researchers and graduate students of multi-agent systems. Industrial practitioners whose work involves system engineering, system control, system biology, and computing science will also find it useful.

From the Inside Flap

A timely guide using iterative learning control (ILC) as a solution for multi-agent systems (MAS) challenges, this book showcases recent advances and industrially relevant applications. Readers are first given a comprehensive overview of the intersection between ILC and MAS, then introduced to a range of topics that include both basic and advanced theoretical discussions, rigorous mathematics, engineering practice, and both linear and nonlinear systems. Through systematic discussion of network theory and intelligent control, the authors explore future research possibilities, develop new tools, and provide numerous applications such as power grids, communication and sensor networks, intelligent transportation systems, and formation control. Readers will gain a roadmap of the latest advances in the fields and can use their newfound knowledge to design their own algorithms.

• Explores the synergy between the important topics of iterative learning control (ILC) and multi-agent systems (MAS)
• Concisely summarizes recent advances and significant applications in ILC methods for power grids, sensor networks, and control processes
• Covers basic theory and rigorous mathematics as well as engineering practice

Written by experienced researchers, Iterative Learning Control for Multi-agent Systems Coordination will appeal to researchers and graduate students of multi-agent systems. Industrial practitioners whose work involves system engineering, system control, system biology, and computing science will also find it useful.

Edition details

• Publisher: Wiley-IEEE Press
• Publication date: 2017
• Language: English
• ISBN 10: 1119189047
• ISBN 13: 9781119189046
• Binding: Hardcover
• Edition number: 1
• Number of pages: 272