Adaptive Dynamic Programming-Based Optimal Interaction Control of Modular Robot Manipulators Under Physical Human-Robot Interaction
Bo Lin Dong, Yuhang Gao, Tianjiao An, Bing Ma, Hucheng Jiang · 2023
In this paper, an adaptive dynamic programming (ADP)-based optimal interaction control scheme is proposed for modular robot manipulators (MRMs) under physical human-robot interaction (pHRI) tasks. Dynamic model of the MRM with multiple degrees of freedom is formulated through the use of joint torque feedback (JTF) technique. The radial basis function neural network (RBFNN) is utilized to carry out human motion intention recognition. Based on the ADP algorithm, the optimal control problem of pHRI task-oriented MRM systems is reconsidered as an optimal trajectory tracking problem. The optimal interaction control strategy for pHRI is obtainable by solving the Hamilton-Jacobin-Bellman (HJB) equation for the coupled human-modular robot interaction system using the ADP algorithm. The ultimately uniformly bounded (UUB) of the trajectory tracking error of the closed-loop MRM system under the pHRI task is proved by the Lyapunov theory. Finally, the effectiveness of the proposed method is verified by experimental results.