Adaptive Impedance Control Based on Neural Network for Electrically-Driven Robotic Systems

Abstract

In this paper, a neural network-based adaptive impedance control (NNAIC) scheme is proposed for controlling electrically-driven robotic systems. The neural network (NN) is used to compensate the robotic model uncertainties and the motor dynamic uncertainties, and a robust compensator term is derived to compensate the disturbances and approximation errors of the NN. In this way, the performances of the joint positions and force tracking can be then improved. Based on the Lyapunov stability theorem, it is proved that the control system is stable and all the signals in closed-loop system are bounded. Simulation tests on a 2-link electrically-driven robotic manipulator are presented to show the effectiveness of the proposed intelligent impedance control method.