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Finite-time adaptive neural control of nonlinear systems with unknown output hysteresis

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  • Li, Zheng
  • Wang, Fang
  • Zhu, Ruitai

Abstract

This paper aims to address the problem of the adaptive finite-time neural control for a class of nonlinear systems with the dynamic disturbance and output hysteresis. The Bouc–Wen model is first introduced to capture the output hysteresis phenomenon. The variable-transformed method is employed to resolve the problem that x1 cannot be available for measurement because of the output hysteresis. Furthermore, for the sake of conquering the output hysteresis constraint, the adaptive backstepping control and ln-type barrier Lyapunov function (BLF) are combined in a unified framework, which can guarantee the prescribed constraint of the tracking error. In addition, the Nussbaum function is used to deal with the unknown control gain problem (UCGP). Basing on the new finite-time stability criterion, an adaptive finite time controller is constructed, which can ensure that the closed-loop system is segi-global practical finite-time stability (SGPFS). The system states remain in the defined compact sets and the output constraint is not violated. Finally, the simulation is implemented to evaluate the effectiveness of the proposed scheme.

Suggested Citation

  • Li, Zheng & Wang, Fang & Zhu, Ruitai, 2021. "Finite-time adaptive neural control of nonlinear systems with unknown output hysteresis," Applied Mathematics and Computation, Elsevier, vol. 403(C).
    • Handle: RePEc:eee:apmaco:v:403:y:2021:i:c:s0096300321002654
    DOI: 10.1016/j.amc.2021.126175
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    References listed on IDEAS

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    1. Yong-Hua Liu & Ying Feng & Xinkai Chen, 2014. "Robust Adaptive Dynamic Surface Control for a Class of Nonlinear Dynamical Systems with Unknown Hysteresis," Abstract and Applied Analysis, Hindawi, vol. 2014, pages 1-10, January.
    2. Xiaonan Xia & Tianping Zhang, 2015. "Adaptive Neural Output Feedback Control of Stochastic Nonlinear Systems with Unmodeled Dynamics," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-13, July.
    3. Ying-Jiu Liang & Ruicheng Ma & Min Wang & Jun Fu, 2015. "Global finite-time stabilisation of a class of switched nonlinear systems," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(16), pages 2897-2904, December.
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    Citations

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    Cited by:

    1. Wu, Ziwen & Zhang, Tianping & Xia, Xiaonan & Hua, Yu, 2022. "Finite-time adaptive neural command filtered control for non-strict feedback uncertain multi-agent systems including prescribed performance and input nonlinearities," Applied Mathematics and Computation, Elsevier, vol. 421(C).
    2. Zhang, Guodong & Cao, Jinde, 2023. "New results on fixed/predefined-time synchronization of delayed fuzzy inertial discontinuous neural networks: Non-reduced order approach," Applied Mathematics and Computation, Elsevier, vol. 440(C).
    3. Cui, Di & Zou, Wencheng & Guo, Jian & Xiang, Zhengrong, 2022. "Neural network-based adaptive finite-time tracking control of switched nonlinear systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    4. Bin Li & Jiahao Zhu & Ranran Zhou & Guoxing Wen, 2022. "Adaptive Neural Network Sliding Mode Control for a Class of SISO Nonlinear Systems," Mathematics, MDPI, vol. 10(7), pages 1-12, April.
    5. Wang, Kun-Peng & Ding, Dong & Tang, Ze & Feng, Jianwen, 2022. "Leader-Following consensus of nonlinear multi-agent systems with hybrid delays: Distributed impulsive pinning strategy," Applied Mathematics and Computation, Elsevier, vol. 424(C).
    6. 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).

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