Human Motion Intention Estimation-Based Decentralized Robust Control of Modular Robot Manipulators

Abstract

In this paper, a decentralized robust control method of modular robot manipulator (MRM) is proposed under the situation of physical human-robot interaction (pHRI). Different from the traditional human-robot interaction control method that relies on the precise dynamic model and centralized control scheme, the decentralized robust control method is proposed based on the human motion intention, which is estimated through radial basis function neural network (RBFNN). By only using local dynamic information of each joint, a decentralized robust controller is designed to deal with various model uncertainties and joint trajectory tracking problems. Based on Lyapunov theory, the asymptotic stability of the closed-loop robotic system is verified. Finally, the effectiveness of the proposed method is verified by experiments.