典型文献
Optimal Synchronization Control of Heterogeneous Asymmetric Input-Constrained Unknown Nonlinear MASs via Reinforcement Learning
文献摘要：
The asymmetric input-constrained optimal synch-ronization problem of heterogeneous unknown nonlinear multiagent systems (MASs) is considered in the paper. Intuitively, a state-space transformation is performed such that satisfaction of symmetric input constraints for the transformed system guarantees satisfaction of asymmetric input constraints for the original system. Then, considering that the leader's information is not available to every follower, a novel distributed observer is designed to estimate the leader's state using only exchange of information among neighboring followers. After that, a network of augmented systems is constructed by combining observers and followers dynamics. A nonquadratic cost function is then leveraged for each augmented system (agent) for which its optimization satisfies input constraints and its corresponding constrained Hamilton-Jacobi-Bellman (HJB) equation is solved in a data-based fashion. More specifically, a data-based off-policy reinforcement learning (RL) algorithm is presented to learn the solution to the constrained HJB equation without requiring the complete knowledge of the agents' dynamics. Convergence of the improved RL algorithm to the solution to the constrained HJB equation is also demonstrated. Finally, the correctness and validity of the theoretical results are demonstrated by a simulation example.
文献关键词：
中图分类号：
作者姓名：
Lina Xia;Qing Li;Ruizhuo Song;Hamidreza Modares
作者机构：
Beijing Engineering Research Center of Industrial Spectrum Imaging,School of Automation and Electrical Engineering,University of Science and Technology Beijing,Beijing 100083,China;Department of Mechanical Engineering,Michigan State University,East Lansing,MI 48824 USA
文献出处：
引用格式：
[1]Lina Xia;Qing Li;Ruizhuo Song;Hamidreza Modares-.Optimal Synchronization Control of Heterogeneous Asymmetric Input-Constrained Unknown Nonlinear MASs via Reinforcement Learning)[J].自动化学报（英文版）,2022(03):520-532
A类：
synch,ronization,multiagent,Intuitively,nonquadratic
B类：
Optimal,Synchronization,Control,Heterogeneous,Asymmetric,Input,Constrained,Unknown,Nonlinear,MASs,via,Reinforcement,Learning,asymmetric,input,constrained,optimal,problem,heterogeneous,unknown,nonlinear,systems,considered,paper,state,space,transformation,performed,such,that,satisfaction,constraints,transformed,guarantees,original,Then,considering,leader,information,not,available,every,novel,distributed,designed,estimate,using,only,exchange,among,neighboring,followers,After,network,augmented,constructed,by,combining,observers,dynamics,cost,function,then,leveraged,each,which,its,optimization,satisfies,corresponding,Hamilton,Jacobi,Bellman,HJB,equation,solved,data,fashion,More,specifically,off,policy,reinforcement,learning,RL,algorithm,presented,solution,without,requiring,complete,knowledge,agents,Convergence,improved,also,demonstrated,Finally,correctness,validity,theoretical,results,are,simulation,example
AB值：
0.586825
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