On reference trajectory modification approach for Cartesian space neural network control of robot manipulators

Abstract

It is well known that computed torque robot control is subjected to performance degradation due to uncertainties in robot model, and application of neural network (NN) compensation techniques are promising. In this paper we examine the effectiveness of NN as a compensator for the complex problem of Cartesian space control. In particular we examine the differences in system performance when the same NN compensator is applied at different locations in the controller. It is found that using NN to modify the reference trajectory to compensate for model uncertainties is much more effective than the traditional approach of modifying joint torque/force. To facilitate the analysis, a new NN training signal is introduced. The study is extended to non-model based Cartesian control problem. Simulation results are also presented.