Robust adaptive distributed optimization for heterogeneous unknown second-order nonlinear multiagent systems  

作　　者：Ping GONG Qing-Guo WANG 

机构地区：[1]School of Mathematics and Statistics,Guangdong University of Foreign Studies,Guangzhou 510006,China [2]Institute of Artificial Intelligence and Future Networks,Beijing Normal University,Zhuhai 519087,China [3]Guangdong Provincial/Zhuhai Key Laboratory of Interdisciplinary Research and Application for Data Science,Beijing Normal University-Hong Kong Baptist University United International College,Zhuhai 519087,China

出　　处：《Science China(Information Sciences)》2025年第4期387-388,共2页中国科学(信息科学)(英文版)

基　　金：supported by National Natural Science Foundation of China(Grant Nos.62003142,62373060);Guangdong Basic and Applied Basic Research Foundation(Grant Nos.2023A1515011025,2020A1515110965);Guangdong Provincial Key Laboratory of IRADS(Grant No.2022B1212010006)。

摘　　要：The distributed optimization problem has drawn significant attention recently since it has a variety of applications in the economic dispatch of smart grids,regression of distributed data,optimal coordination of mobile robot systems(MRSs),and so on.Different from the traditional leaderless consensus and consensus tracking problems in multiagent systems(MASs),the typical distributed optimization problems are not only required to achieve consensus but also to cooperatively minimize a global cost function[1],where the global cost function is the sum of local cost functions and each local cost function is only known to the agent itself.

关 键 词：mobile robot systems mrss distributed optimization problems multiagent systems mass economic dispatch smart gridsregression distributed dataoptimal coordination distributed optimization problem robust adaptive distributed optimization cooperatively minimize global cost function whe heterogeneous unknown second order nonlinear multiagent systems 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]