A new adaptive fuzzy neural force controller for robots manipulator interacting with environments

Abstract

In this paper, a fuzzy neural network-based adaptive force control scheme for an it-link robot manipulator under an unknown environment is proposed. The dynamics model of the robot manipulator and the environment stiffness coefficient are assumed to be not exactly known in applications. Therefore, the traditional adaptive impedance force controller is not valid. In this study, the fuzzy neural systems (FNSs) are adopted to estimate the model of robot manipulator to propose an adaptive scheme to accomplish the tracking control problem. Based on the Lyapunov stability theory, the stability of the robot manipulator is guaranteed and the corresponding update laws of FNSs' parameters and stiffness coefficient of the environment can be obtained. Finally, simulation results of two-link robot manipulator contact with environment are introduced to illustrate the performance and effectiveness of our approach.