汉密尔顿-雅各比-贝尔曼方程
计算机科学
李雅普诺夫函数
数学优化
微分博弈
最优控制
动态规划
模块化设计
机器人
控制理论(社会学)
数学
控制(管理)
人工智能
操作系统
物理
量子力学
非线性系统
作者
Tianjiao An,Yuexi Wang,Guangjun Liu,Yuanchun Li,Bo Dong
出处
期刊:IEEE transactions on cybernetics
[Institute of Electrical and Electronics Engineers]
日期:2023-07-01
卷期号:53 (7): 4691-4703
被引量:15
标识
DOI:10.1109/tcyb.2023.3277558
摘要
Major challenges of controlling human–robot collaboration (HRC)-oriented modular robot manipulators (MRMs) include the estimation of human motion intention while cooperating with a robot and performance optimization. This article proposes a cooperative game-based approximate optimal control method of MRMs for HRC tasks. A harmonic drive compliance model-based human motion intention estimation method is developed using robot position measurements only, which forms the basis of the MRM dynamic model. Based on the cooperative differential game strategy, the optimal control problem of HRC-oriented MRM systems is transformed into a cooperative game problem of multiple subsystems. By taking advantage of the adaptive dynamic programming (ADP) algorithm, a joint cost function identifier is developed via the critic neural networks, which is implemented for solving the parametric Hamilton–Jacobi–Bellman (HJB) equation and Pareto optimal solutions. The trajectory tracking error under the HRC task of the closed-loop MRM system is proved to be ultimately uniformly bounded (UUB) by the Lyapunov theory. Finally, experiment results are presented, which reveal the advantage of the proposed method.
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