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Iterative learning control for multi-agent systems coordination

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 ran...

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Detalles Bibliográficos
Autores principales: Yang, Shiping, Xu, Jian-Xin, Li, Xuefang, Shen, Dong
Lenguaje:eng
Publicado: Wiley-IEEE Press 2016
Materias:
Acceso en línea:http://cds.cern.ch/record/2259084
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author Yang, Shiping
Xu, Jian-Xin
Li, Xuefang
Shen, Dong
author_facet Yang, Shiping
Xu, Jian-Xin
Li, Xuefang
Shen, Dong
author_sort Yang, Shiping
collection CERN
description 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.
id cern-2259084
institution Organización Europea para la Investigación Nuclear
language eng
publishDate 2016
publisher Wiley-IEEE Press
record_format invenio
spelling cern-22590842021-04-21T19:16:47Zhttp://cds.cern.ch/record/2259084engYang, ShipingXu, Jian-XinLi, XuefangShen, DongIterative learning control for multi-agent systems coordinationEngineeringA 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.Wiley-IEEE Pressoai:cds.cern.ch:22590842016
spellingShingle Engineering
Yang, Shiping
Xu, Jian-Xin
Li, Xuefang
Shen, Dong
Iterative learning control for multi-agent systems coordination
title Iterative learning control for multi-agent systems coordination
title_full Iterative learning control for multi-agent systems coordination
title_fullStr Iterative learning control for multi-agent systems coordination
title_full_unstemmed Iterative learning control for multi-agent systems coordination
title_short Iterative learning control for multi-agent systems coordination
title_sort iterative learning control for multi-agent systems coordination
topic Engineering
url http://cds.cern.ch/record/2259084
work_keys_str_mv AT yangshiping iterativelearningcontrolformultiagentsystemscoordination
AT xujianxin iterativelearningcontrolformultiagentsystemscoordination
AT lixuefang iterativelearningcontrolformultiagentsystemscoordination
AT shendong iterativelearningcontrolformultiagentsystemscoordination