Heuristic dynamic programming-based learning control for discrete-time disturbed multi-agent systems
Yao Zhang1
Chaoxu Mu1
Yong Zhang1
Yanghe Feng2
1.School of Electrical and Information Engineering,Tianjin University,Tianjin 300072,China2.College of Systems Engineering,National University of Defense Technology,Changsha 410073,Hunan,China
摘要：Owing to extensive applications in many fields, the synchronization problem has been widely investigated in multi-agent systems. The synchronization for multi-agent systems is a pivotal issue, which means that under the designed control policy, the output of systems or the state of each agent can be consistent with the leader. The purpose of this paper is to investigate a heuristic dynamic programming (HDP)-based learning tracking control for discrete-time multi-agent systems to achieve synchronization while considering disturbances in systems. Besides, due to the difficulty of solving the coupled Hamilton–Jacobi–Bellman equation analytically, an improved HDP learning control algorithm is proposed to realize the synchronization between the leader and all following agents, which is executed by an action-critic neural network. The action and critic neural network are utilized to learn the optimal control policy and cost function, respectively, by means of introducing an auxiliary action network. Finally, two numerical examples and a practical application of mobile robots are presented to demonstrate the control performance of the HDP-based learning control algorithm.
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论文发表日期：2021-08-05
在线出版日期：2025-08-15（本平台首次上网日期，不代表文献的发表时间）
页数：15( 339-353 )
英文信息