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Distributed cooperative control and optimization for multi-agent systems

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Distributed cooperative control and optimization for multi-agent systems/ by Qing Wang, Bin Xin, Jie Chen.
作者:	Wang, Qing.
其他作者:	Xin, Bin.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	ix, 149 p. :ill. (chiefly color), digital ; : 24 cm.;
Contained By:	Springer Nature eBook
標題:	Multiagent systems. -
電子資源:	https://doi.org/10.1007/978-981-96-0950-5
ISBN:	9789819609505

Distributed cooperative control and optimization for multi-agent systems
Wang, Qing.

Distributed cooperative control and optimization for multi-agent systems[electronic resource] /by Qing Wang, Bin Xin, Jie Chen. - Singapore :Springer Nature Singapore :2025. - ix, 149 p. :ill. (chiefly color), digital ;24 cm.

Introduction -- Disturbance observer-based sliding mode control for multi-agent systems with mismatched uncertainties -- Observer-based containment for a class of nonlinear multi-agent systems with time-delayed protocols -- Robust output containment control of multi-agent systems with unknown heterogeneous nonlinear uncertainties in directed networks -- Distributed event-based consensus control of multi-agent system with matching nonlinear uncertainties.

This book provides a concise and in-depth exposition of distributed control and optimization problems of multi-agent systems. The book integrates various ideas and tools from dynamic systems, control theory, graph theory, and optimization to address the special challenges posed by such complexities in the environment as communication delay, topological dynamics, and environmental uncertainties. In order to deal with the mismatched uncertainties and time delay, observer-based controller and sliding mode control are developed to achieve consensus control. When there is a leader or multiple leaders in the communication topologies, containment control is required. The book studies both state and output containment for nonlinear multi-agent systems with undirected or directed networks. Furthermore, event-triggered schemes are proposed to reduce communication and computation costs. Distributed optimization for multi-agent systems is an interesting topic that has attracted more and more attention due to its wide range of applications such as smart grids, sensor networks, and mobile manipulators. In distributed optimization, the goal is to optimize the global cost function, which is the sum of all local cost functions, each of which is known only by its own local agent. Distributed nonsmooth convex optimization for multi-agent systems based on proximal operators is developed to achieve distributed optimal consensus.

ISBN: 9789819609505

Standard No.: 10.1007/978-981-96-0950-5doiSubjects--Topical Terms:

745736
Multiagent systems.

LC Class. No.: TJ215.5

Dewey Class. No.: 006.30285436

Distributed cooperative control and optimization for multi-agent systems
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520 $a This book provides a concise and in-depth exposition of distributed control and optimization problems of multi-agent systems. The book integrates various ideas and tools from dynamic systems, control theory, graph theory, and optimization to address the special challenges posed by such complexities in the environment as communication delay, topological dynamics, and environmental uncertainties. In order to deal with the mismatched uncertainties and time delay, observer-based controller and sliding mode control are developed to achieve consensus control. When there is a leader or multiple leaders in the communication topologies, containment control is required. The book studies both state and output containment for nonlinear multi-agent systems with undirected or directed networks. Furthermore, event-triggered schemes are proposed to reduce communication and computation costs. Distributed optimization for multi-agent systems is an interesting topic that has attracted more and more attention due to its wide range of applications such as smart grids, sensor networks, and mobile manipulators. In distributed optimization, the goal is to optimize the global cost function, which is the sum of all local cost functions, each of which is known only by its own local agent. Distributed nonsmooth convex optimization for multi-agent systems based on proximal operators is developed to achieve distributed optimal consensus.
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