Dynamic velocity feed-forward compensation control with RBF-NN system identification for industrial robots

Abstract

A dynamic velocity feed-forward compensation control (DVFCC) approach with RBF neural network (RBF-NN) dynamic model identification was presented for the adaptive trajectory tracking of industrial robots. The proposed control approach combined the advantages of traditional feedback closed-loop position control and computed torque control based on inverse dynamic model. The feed-forward compensator used a nominal robot dynamics as accurate dynamic model and on-line identification with RBF-NN as uncertain part to improve dynamic modeling accuracy. The proposed compensation was applied as velocity feed-forward by an inverse velocity controller that can convert torque signal into velocity in the standard industrial controller. Then, the need for a torque control interface was avoided in the real-time dynamic control of industrial robot. The simulations and experiments were carried out on a gas cutting manipulator. The results show that the proposed control approach can reduce steady-state error, suppress overshoot and enhance tracking accuracy and efficiency in joint space and Cartesian space, especially under highspeed condition.