Abstract
Recently, based on multi-resolution analysis, wavelet neural networks (WNN) have been proposed as an alternative to NN for approximating arbitrary nonlinear functions in L 2 (R). Discontinuous friction function is an unavoidable nonlinear effect that can limit control performance in mechanical systems. In this paper, adaptive WNN is used to design a friction compensator for a single joint mechanical system. Then asymptotically stability of the system is assured by adding a PD controller and adaptive robust terms. The simulation results show the validity of the control scheme. © Springer-Verlag Berlin Heidelberg 2006.
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CITATION STYLE
Song, S. M., Song, Z. Y., Chen, X. L., & Duan, G. (2006). Adaptive wavelet neural network friction compensation of mechanical systems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3972 LNCS, pp. 1131–1139). Springer Verlag. https://doi.org/10.1007/11760023_166
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