Neural network techniques for robust force control of robot manipulators

Abstract

In this paper a neural network force/position control scheme is proposed to compensate uncertainties in both robot dynamics and unknown environments. The proposed impedance control allows us to regulate force directly by specifying a desired force. Training signals are proposed for a feedforward neural network controller. The robustness analysis of the uncertainties in environment position is presented. Simulation results are presented to show that both the position and force tracking are excellent in the presence of uncertainties in robot dynamics and unknown environments.