Robust Neural-Network Compensating Control for Robot Manipulator Based on Computed Torque Control

Abstract

This paper proposes a new controller design approach for trajectory tracking of robot manipulator with uncertainties. The proposed controller is based on the computed torque control structure, and incorporates a compensator, which is realized by Functional Link Neural Network, and a robustifying term. In addition, when neural newtork reconstruction error is not uniformly bounded, an adaptive robustifying term is designed. It is shown that all the signals in the closed loop system are uniformly ultimately bounded. Compared with other approaches, no joint acceleration measurement and exactly known inertia matrix are required. Both theory and simulation results show the effectiveness of the proposed controller.