Robust adaptive position and force controller design of robot manipulator using fuzzy neural networks

This paper presents a robust adaptive position and force control scheme for an n-link robot manipulator under unknown environment. The robot manipulator's model and the stiffness coefficient of contact environment are assumed to be not exactly known. Therefore, the traditional impedance force controller cannot be applied. We herein adopt the fuzzy neural networks (FNNs) to estimate the unknown model matrices of robot manipulator and the adaptive tracking position and force control is developed by the proposed adaptive scheme. Based on the Lyapunov stability theory, the stability of the closed-loop system and convergence of adjustable parameters are guaranteed. The corresponding update laws of FNNs' parameters and estimated stiffness coefficient of contacting environment can be derived. Finally, simulation results of a two-link robot manipulator with environment constraint are introduced to illustrate the performance and effectiveness of our approach.