LEADER 03602nam 22005295 450 001 9910984582403321 005 20250302115236.0 010 $a9789819609505$b(electronic bk.) 010 $z9789819609499 024 7 $a10.1007/978-981-96-0950-5 035 $a(MiAaPQ)EBC31928888 035 $a(Au-PeEL)EBL31928888 035 $a(CKB)37744251300041 035 $a(DE-He213)978-981-96-0950-5 035 $a(OCoLC)1503953841 035 $a(EXLCZ)9937744251300041 100 $a20250302d2025 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aDistributed Cooperative Control and Optimization for Multi-Agent Systems /$fby Qing Wang, Bin Xin, Jie Chen 205 $a1st ed. 2025. 210 1$aSingapore :$cSpringer Nature Singapore :$cImprint: Springer,$d2025. 215 $a1 online resource (222 pages) 311 08$aPrint version: Wang, Qing Distributed Cooperative Control and Optimization for Multi-Agent Systems Singapore : Springer,c2025 9789819609499 327 $aIntroduction -- 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. 330 $aThis 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. 606 $aAutomation 606 $aAutomatic control 606 $aAutomation 606 $aControl and Systems Theory 615 0$aAutomation. 615 0$aAutomatic control. 615 14$aAutomation. 615 24$aControl and Systems Theory. 676 $a629.8 700 $aWang$b Qing$01243072 701 $aXin$b Bin$01437759 701 $aChen$b Jie$01299851 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 912 $a9910984582403321 996 $aDistributed Cooperative Control and Optimization for Multi-Agent Systems$94326132 997 $aUNINA