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.

中文翻译：

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基于协作博弈的模块化机器人机械手的人机协作近似最优控制


控制面向人机协作（HRC）的模块化机器人操纵器（MRM）的主要挑战包括在与机器人协作时估计人体运动意图以及性能优化。本文提出了一种用于 HRC 任务的基于协作博弈的 MRM 近似最优控制方法。仅使用机器人位置测量开发了基于谐波驱动顺应模型的人体运动意图估计方法，该方法构成了 MRM 动态模型的基础。基于合作微分博弈策略，将面向HRC的MRM系统的最优控制问题转化为多个子系统的合作博弈问题。利用自适应动态规划（ADP）算法，通过批评神经网络开发了联合成本函数标识符，用于求解参数汉密尔顿-雅可比-贝尔曼（HJB）方程和帕累托最优解。通过Lyapunov理论证明了闭环MRM系统在HRC任务下的轨迹跟踪误差最终是一致有界的（UUB）。最后给出了实验结果，揭示了该方法的优点。