On-line Neural Network Compensator for Constrained Robot Manipulators

In this paper, a new neural network controller for the constrained robot manipulators in task space is presented. The neural network will be used for adaptive compensation of the structured and unstructured uncertainties. The controller consisted of a model-based term and a neural network on-line adaptive compensation term. It is shown that the neural network adaptive compensation is universally able to cope with totally different classes of system uncertainties. Novel adaptive learning algorithms for tuning the weights of neural network are proposed. A suitable error filtered signal for training the neural network can be easily obtained from the controller design without using any model knowledge of the robot manipulator itself The closed-loop system with neural network adaptation on line is guaranteed to be stable in the Lyapunov sense. Detailed simulation results are given to show the effectiveness of the proposed controller.