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Abstract

This paper is concerned with the application of neural networks for adaptive compensation of the structured and unstructured uncertainties of the robot manipulator. The controller consists of a model-based term and a neural network on-line adaptive compensation term. It is shown that the neural network 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 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.

Keywords

neural network compensator robot manipulator adaptive control

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

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