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Autor(en): 
  • Jie Chen
  • Qing Wang
  • Bin Xin
  • Distributed Cooperative Control and Optimization for Multi-Agent Systems 
     

    (Buch)
    Dieser Artikel gilt, aufgrund seiner Grösse, beim Versand als 2 Artikel!


    Übersicht

    Auf mobile öffnen
     
    Lieferstatus:   i.d.R. innert 5-10 Tagen versandfertig
    Veröffentlichung:  März 2026  
    Genre:  Naturwissensch., Medizin, Technik 
     
    chatteringreduction / DisturbanceObserver / Euler-Lagrangesystem / finite-timeconsensus / mismatcheduncertainties / Multi-agentsystem(MAS) / outputcontainment / Slidingmodecontrol
    ISBN:  9789819609529 
    EAN-Code: 
    9789819609529 
    Verlag:  Springer 
    Einband:  Kartoniert  
    Sprache:  English  
    Dimensionen:  H 235 mm / B 155 mm / D 9 mm 
    Gewicht:  254 gr 
    Seiten:  160 
    Bewertung: Titel bewerten / Meinung schreiben
    Inhalt:
    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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