Adaptive Reinforcement Learning-Enhanced Motion/Force Control Strategy for Multirobot Systems

Phuong Nam Dao, Duy Khanh Do and Dinh Khue Nguyen

Mathematical Problems in Engineering, 2021, vol. 2021, 1-18

Abstract:

This paper presents an adaptive reinforcement learning- (ARL-) based motion/force tracking control scheme consisting of the optimal motion dynamic control law and force control scheme for multimanipulator systems. Specifically, a new additional term and appropriate state vector are employed in designing the ARL technique for time-varying dynamical systems with online actor/critic algorithm to be established by minimizing the squared Bellman error. Additionally, the force control law is designed after obtaining the computation of constraint force coefficient by the Moore–Penrose pseudo-inverse matrix. The tracking effectiveness of the ARL-based optimal control is verified in the closed-loop system by theoretical analysis. Finally, simulation studies are conducted on a system of three manipulators to validate the physical realization of the proposed optimal tracking control design.

Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:hin:jnlmpe:5560277

DOI: 10.1155/2021/5560277

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