Composite neural dynamic surface control of a class of uncertain nonlinear systems in strict-feedback form

Bin Xu, Zhongke Shi, Chenguang Yang, Fuchun Sun

科研成果: 期刊稿件文章同行评审

369 引用 (Scopus)

摘要

This paper studies the composite adaptive tracking control for a class of uncertain nonlinear systems in strict-feedback form. Dynamic surface control technique is incorporated into radial-basis-function neural networks (NNs)-based control framework to eliminate the problem of explosion of complexity. To avoid the analytic computation, the command filter is employed to produce the command signals and their derivatives. Different from directly toward the asymptotic tracking, the accuracy of the identified neural models is taken into consideration. The prediction error between system state and serial-parallel estimation model is combined with compensated tracking error to construct the composite laws for NN weights updating. The uniformly ultimate boundedness stability is established using Lyapunov method. Simulation results are presented to demonstrate that the proposed method achieves smoother parameter adaption, better accuracy, and improved performance.

源语言英语
文章编号6783745
页(从-至)2626-2634
页数9
期刊IEEE Transactions on Cybernetics
44
12
DOI
出版状态已出版 - 1 12月 2014

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