A stable adaptive neural network control approach is proposed in this paper for uncertain nonlinear strict-feedback systems based on backstepping. The key assumptions are that the neural network approximation errors satisfy certain bounding conditions. By a special scheme, the controller singularity problem is avoided perfectly. The proposed scheme improves the control performance of systems and extends the application scope of nonlinear systems. The overall neural network control systems guarantee that all the signals of the systems are uniformly ultimately bounded and the tracking error converges to a small neighborhood of zero by suitably choosing the design parameter. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Liu, Y. J., & Wang, W. (2006). Adaptive neural network control for nonlinear systems based on approximation errors. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3972 LNCS, pp. 836–841). Springer Verlag. https://doi.org/10.1007/11760023_124
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