Iterative Learning Control for Multi-Agent Systems Coordination (Wiley - IEEE)

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

ISBN 10: 1119189047 / ISBN 13: 9781119189046

Published by Wiley-Blackwell 2016-11-07, 2016

About this Item

Seller Inventory # 6666-WLY-9781119189046

Bibliographic Details

Title: Iterative Learning Control for Multi-Agent ...

Publisher: Wiley-Blackwell 2016-11-07

Publication Date: 2016

Binding: Hardcover

Condition: New

About this title

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

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.