An Adaptive Robust Hybrid Force/Position Control for Robot Manipulators System Subject to Mismatched and Matched Disturbances

Abstract

A novel adaptive robust hybrid force/position control (ARHFPC) strategy is proposed for robot manipulator systems subject to dynamic uncertainties and unknown matched and mismatched disturbances under input saturation. First, the position controller is designed based on the backstepping approach. The first-order low-pass filter and the auxiliary dynamic system are synthesized into the controller to overcome the complex derivative operation of virtual control and handle the effect of input saturation, respectively. Radial basis function neural networks (RBFNNs) are utilized to approximate the dynamic uncertainties and matched disturbances. Then, a disturbance observer is designed for the mismatched disturbances. To enhance control accuracy of the interaction force between the end-effector and the external environment, a fuzzy proportional-integral (FPI) control scheme is presented. Theoretical analysis proves that all signals in the closed-loop control system of robot manipulators are locally uniformly ultimately bounded (UUB). Simulation results demonstrate the effectiveness and robustness of the proposed control scheme.