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Adaptive tracking control of nonlinear systems with dynamic uncertainties using neural network

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  • Yu-Qun Han

Abstract

In this paper, an adaptive neural tracking control approach is proposed for a class of nonlinear systems with dynamic uncertainties. The radial basis function neural networks (RBFNNs) are used to estimate the unknown nonlinear uncertainties, and then a novel adaptive neural scheme is developed, via backstepping technique. In the controller design, instead of using RBFNN to approximate each unknown function, we lump all unknown functions into a suitable unknown function that is approximated by only a RBFNN in each step of the backstepping. It is shown that the designed controller can guarantee that all signals in the closed-loop system are semi-globally bounded and the tracking error finally converges to a small domain around the origin. Two examples are given to demonstrate the effectiveness of the proposed control scheme.

Suggested Citation

  • Yu-Qun Han, 2018. "Adaptive tracking control of nonlinear systems with dynamic uncertainties using neural network," International Journal of Systems Science, Taylor & Francis Journals, vol. 49(7), pages 1391-1402, May.
    • Handle: RePEc:taf:tsysxx:v:49:y:2018:i:7:p:1391-1402
    DOI: 10.1080/00207721.2018.1453955
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    Cited by:

    1. Liu, Shanlin & Niu, Ben & Zong, Guangdeng & Zhao, Xudong & Xu, Ning, 2022. "Adaptive fixed-time hierarchical sliding mode control for switched under-actuated systems with dead-zone constraints via event-triggered strategy," Applied Mathematics and Computation, Elsevier, vol. 435(C).
    2. Qi, Wenhai & Zong, Guangdeng & Cheng, Jun & Jiao, Ticao, 2019. "Robust finite-time stabilization for positive delayed semi-Markovian switching systems," Applied Mathematics and Computation, Elsevier, vol. 351(C), pages 139-152.

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