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.
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References
Kang, M.S.: Robust Digital Friction Compensation. Control Engineering Practice 6, 359–367 (1998)
Armstrong, B., Dupont, P.C., De Canudas, W.: A Survey of Models, Analysis Tools and Compensation Methods for Control of Machines With Friction. Automatica 30, 1083–1138 (1994)
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)
Selmic, R.R., Lewis, F.L.: Neural-network Approximation of Piecewise Continuous Functions: Application to Friction Compensation. IEEE Transactions on Neural Networks 13, 745–750 (2002)
Armstrong, B.: Friction: Experimental Determination, Modeling and Compensation. In: IEEE International Conference on Robotics and Automation, Philadelphia, pp. 1422–1427 (1998)
Broomhead, D., Lowe., D.: Multivariable Interpolation and Adaptive Networks. Complex Systems 2, 321–355 (1988)
Sun, Y.L., Sarat, N.P.: Neuro-controller Design for Nonlinear Fighter Aircraft Maneuver Using Fully Tuned RBF Neural Networks. Automatica 37, 1293–1301 (2001)
Park, J., Sandberg, I.W.: Universal Approximation Using Radial-Basis-Function Neural Networks. Neural Computat. 3, 246–257 (1990)
Reyes, F., Kelly, R.: Experimental Evaluation of Model-Based Controllers on a Direct-Drive Robot Arm. Mechatronics 11, 267–282 (2001)
Wang, Y., Chai, T.: Compensating Modeling and Control for Friction Using Adaptive Fuzzy System. In: Proceedings of the IEEE CDC, pp. 5117–5121 (2004)
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© 2005 Springer-Verlag Berlin Heidelberg
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Wang, Y., Chai, T., Zhao, L., Tie, M. (2005). Compensating Modeling and Control for Friction Using RBF Adaptive Neural Networks. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_26
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DOI: https://doi.org/10.1007/11427469_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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