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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...
Autores principales: | , , , |
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Lenguaje: | eng |
Publicado: |
Wiley-IEEE Press
2016
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Materias: | |
Acceso en línea: | http://cds.cern.ch/record/2259084 |
_version_ | 1780953924331634688 |
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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 |