Fuzzy neural network-based adaptive impedance force control design of robot manipulator under unknown environment

In this paper, an adaptive impedance force control scheme for an n-link robot manipulator under unknown environment is proposed. The system dynamics of the robot manipulator is assumed that system model is not exactly known or has system uncertainty. Therefore, the traditional adaptive impedance force controller is not valid. Herein, the fuzzy neural networks are adopted to estimate the system model terms of robot and the force tracking control is developed by the proposed adaptive scheme. The proposed scheme is established by gradient descent approach. Using the Lyapunov stability theory, the update laws of fuzzy neural networks can be derived and the stability of the closed-loop system is guaranteed. Finally, simulation results of a two-link robot manipulator with environment constraint are introduced to illustrate the performance and effectiveness of our approach.