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Design of Fixed-Time Neural Controller for Uncertain Nonstrict-Feedback Systems with Smooth Switching Functions

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Abstract

The tracking problem of uncertain nonstrict-feedback nonlinear systems (UNFNS) is examined to develop a novel adaptive neural control scheme to ensure fixed-time convergence. In particular, the challenge associated with the unknown nonlinear function can be overcome through neural network (NN) based estimation. Therefore, an NN-based adaptive fixed-time control scheme is established with only one parameter, using the property of the basis function vector to address the algebraic loop problem. Furthermore, the singularity problem can be solved by incorporating a smooth switching function. A rigorous theoretical analysis is performed to demonstrate that the output signal can track the reference signal within a fixed time and that the signals in the control systems are bounded. Finally, numerical simulations are performed to validate the feasibility of the proposed methodology.

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Author information

Authors and Affiliations

  1. State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China

    Jinzi Yang

  2. College of Science, Liaoning University of Technology, Jinzhou, 121001, China

    Yuanxin Li & Shaocheng Tong

Authors
  1. Jinzi Yang
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  2. Yuanxin Li
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  3. Shaocheng Tong
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Corresponding author

Correspondence to Yuanxin Li.

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The authors declare no conflict of interest.

Additional information

This work was supported in part by the National Science of China under Grant Nos. 62373176, 61973146, and in part by the Applied Basic Research Program in Liaoning Province under Grant No. 2022JH2/101300276, and in part by the Key Project of the Educational Department of Liaoning Province under Grant No. JYTZD2023084, and in part by Taishan Scholar Project of Shandong Province of China under Grant No. tsqn201909097.

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Yang, J., Li, Y. & Tong, S. Design of Fixed-Time Neural Controller for Uncertain Nonstrict-Feedback Systems with Smooth Switching Functions. J Syst Sci Complex 36, 2344–2363 (2023). https://doi.org/10.1007/s11424-023-2167-z

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  • DOI: https://doi.org/10.1007/s11424-023-2167-z

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