Performance improvement of robot manipulator control using an on-line neural network compensator

This paper is concerned with the application of neural networks for adaptiv e compensation of the structured and unstructured uncertainties of the robo t manipulator. The controller consists of a model-based term and a neural n etwork on-line adaptive compensation term. It is shown that the neural netw ork adaptive compensation is a universal scheme which is able to cope with totally different classes of system uncertainties. Novel adaptive learning algorithms for tuning the weights of the neural network are proposed. A sui table error filtered signal for training the neural network can be easily o btained from the controller design without using any model knowledge of the robot manipulator itself. The closed-loop system with neural network adapt ation on line is guaranteed to be stable in the Lyapunov sense.