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Neural Networks-Based Adaptive Dynamic Surface Control for Nonlinear Pure-Feedback Systems under Prescribed Performance and Zero Tracking Error

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Abstract

This article is devoted to solving adaptive tracking control problems for nonlinear pure-feedback systems (NPFSs) subject to prescribed performance and zero tracking error. Firstly, the implicit functions in the system are transformed into explicit functions which can be directly used in the backstepping method by means of the mean value theorem. Second, a variable containing the specified performance function is constructed to constrain the tracking error to always be within the predefined boundaries. Then, the nonlinear filter with compensation information is developed to remove the “explosion of complexity" issue caused by virtual control derivation. By integrating the backstepping method with Lyapunov analysis theory, an adaptive prescribed performance controller is recursively designed, which guarantees the boundedness of all system signals. Meanwhile, the tracking error always remains within the predefined function and asymptotically converges to zero. Finally, the effectiveness of theoretical achievements is substantiated via two simulation experiments.

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Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study.

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  1. College of Mathematical Sciences, Harbin Engineering University, Harbin, 150001, China

    Kun Jiang & Xuxi Zhang

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  1. Kun Jiang
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  2. Xuxi Zhang
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Correspondence to Xuxi Zhang.

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Jiang, K., Zhang, X. Neural Networks-Based Adaptive Dynamic Surface Control for Nonlinear Pure-Feedback Systems under Prescribed Performance and Zero Tracking Error. Circuits Syst Signal Process 42, 7117–7141 (2023). https://doi.org/10.1007/s00034-023-02451-x

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