Abstract
This paper addresses the adaptive neural tracking control problem for a class of nonlinear time delays systems. Radial basis function (RBF) neural networks are used to approximate unknown nonlinear functions, then the adaptive neural network controller is designed by using the dynamic surface control (DSC) technique and Lyapunov-Krasovskii functionals. The “explosion of complexity” problem has been eliminated by using DSC technique. Moreover, the proposed controller guarantees that all the signals in the closed-loop system are bounded and the system output converges to a small neighborhood of the desired reference signal. Finally, simulation results are used to demonstrate the effectiveness of the approach.
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Wang, Sy., Ji, G., Hua, Xb. (2011). Adaptive Tracking Control for a Class of Perturbed Nonlinear Time-Delay Systems. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds) Artificial Intelligence and Computational Intelligence. AICI 2011. Lecture Notes in Computer Science(), vol 7003. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23887-1_20
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DOI: https://doi.org/10.1007/978-3-642-23887-1_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23886-4
Online ISBN: 978-3-642-23887-1
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