Compensating Modeling and Control for Friction Using RBF Adaptive Neural Networks
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.
KeywordsRadial Basis Function Tracking Error Friction Model Basis Function Neural Network Radial Neural Network Controller
Unable to display preview. Download preview PDF.
- 3.de Canudas, W., Noel, C.P., Aubin, A., Brogliato, B.: Adaptive Friction Compensation in Robot Manipulators: Low velocities. Int. J. Robot. Res. 10, 35–41 (1991)Google Scholar
- 5.Armstrong, B.: Friction: Experimental Determination, Modeling and Compensation. In: IEEE International Conference on Robotics and Automation, Philadelphia, pp. 1422–1427 (1998)Google Scholar
- 10.Wang, Y., Chai, T.: Compensating Modeling and Control for Friction Using Adaptive Fuzzy System. In: Proceedings of the IEEE CDC, pp. 5117–5121 (2004)Google Scholar