Neural network identification, predictive modeling and control with a sliding mode learning mechanism: an application to the robotic manipulators

Abstract

The features of a novel adaptive PID-like neurocontrol scheme for nonlinear plants are presented. The controller tuning is based on an estimate of the command-error determined via one-step-ahead neural predictive model of the plant. An on-line learning sliding mode algorithm is applied to the model and to the controller as well. The control architecture developed has been simulated and its effect on the trajectory tracking performance of a simple two-degree-of-freedom robot manipulator has been evaluated. The results show that both learning structures, the neural predictive model and the controller, inherit some of the advantages of SMC: high speed of learning and robustness.