Compensating modeling and control for friction using RBF adaptive neural networks

Authors:
Yongfu Wang;Tianyou Chai;Lijie Zhao;Ming Tie
Affiliations:
Research Center of Automation, Northeastern University, Shenyang, Liaoning, China;Research Center of Automation, Northeastern University, Shenyang, Liaoning, China;Research Center of Automation, Northeastern University, Shenyang, Liaoning, China;Research Center of Automation, Northeastern University, Shenyang, Liaoning, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

This paper presents an application of a radial basis functions adaptive neural networks for compensating the effects induced by the friction in mechanical system. An adaptive neural networks based on radial basis functions is employed, and a bound on the tracking error is derived from the analysis of the tracking error dynamics. The hybrid controller is a combination of a PD+G controller and a neural networks controller which compensates for nonlinear friction. The proposed scheme is simulated on a single link robot control system. The algorithm and simulations results are described.