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Adaptive control design for uncertain switched nonstrict-feedback nonlinear systems to achieve asymptotic tracking performance

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  • Xu, Ning
  • Zhao, Xudong
  • Zong, Guangdeng
  • Wang, Yuanqing

Abstract

In this paper, the adaptive asymptotic tracking control issue is addressed for uncertain switched nonlinear systems with nonstrict-feedback form under the dynamic surface control (DSC) framework. In contrast to most traditional control schemes that can only achieve bounded error tracking performances, the proposed control method in this paper can make the switched systems with unknown nonlinear functions achieve an asymptotic tracking performance. It is completed by introducing nonlinear filters with a compensation term to remove the boundary layer error stemming from using linear filters in the DSC process. Meanwhile, the approximation error caused by the use of radial basis function (RBF) neural networks (NNs) is compensated by an online updated parameter. Furthermore, the nonstrict-feedback form is handled by adopting the inherent properties of RBF NNs. Then, in terms of the backstepping technique and the common Lyapunov function (CLF) approach, the desired control law with only two adaptive laws is set up. Finally, the validity of the developed strategy is verified via simulation results.

Suggested Citation

  • Xu, Ning & Zhao, Xudong & Zong, Guangdeng & Wang, Yuanqing, 2021. "Adaptive control design for uncertain switched nonstrict-feedback nonlinear systems to achieve asymptotic tracking performance," Applied Mathematics and Computation, Elsevier, vol. 408(C).
    • Handle: RePEc:eee:apmaco:v:408:y:2021:i:c:s0096300321004331
    DOI: 10.1016/j.amc.2021.126344
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    References listed on IDEAS

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    1. Xuemiao Chen & Qianjin Zhao & Chunsheng Zhang & Jian Wu, 2019. "Adaptive Asymptotic Tracking Control for a Class of Uncertain Switched Systems via Dynamic Surface Technique," Complexity, Hindawi, vol. 2019, pages 1-9, October.
    2. Chen, Zhongyu & Niu, Ben & Zhao, Xudong & Zhang, Liang & Xu, Ning, 2021. "Model-Based adaptive event-Triggered control of nonlinear continuous-Time systems," Applied Mathematics and Computation, Elsevier, vol. 408(C).
    3. Jidong Wang & Lengxue Zhu & Xiaoping Si, 2017. "Adaptive Neural Tracking Control for Discrete-Time Switched Nonlinear Systems with Dead Zone Inputs," Complexity, Hindawi, vol. 2017, pages 1-8, March.
    4. Iman Zamani & Masoud Shafiee & Asier Ibeas, 2014. "Stability analysis of hybrid switched nonlinear singular time-delay systems with stable and unstable subsystems," International Journal of Systems Science, Taylor & Francis Journals, vol. 45(5), pages 1128-1144, May.
    5. Wang, Yuanqing & Xu, Ning & Liu, Yajuan & Zhao, Xudong, 2021. "Adaptive fault-tolerant control for switched nonlinear systems based on command filter technique," Applied Mathematics and Computation, Elsevier, vol. 392(C).
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    Cited by:

    1. 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).
    2. Yan, Yan & Wu, Libing & Yan, Weijun & Hu, Yuhan & Zhao, Nannan & Chen, Ming, 2022. "Finite-time event-triggered fault-tolerant control for a family of pure-feedback systems," Applied Mathematics and Computation, Elsevier, vol. 426(C).
    3. Chen, Zhongyu & Niu, Ben & Zhao, Xudong & Zhang, Liang & Xu, Ning, 2021. "Model-Based adaptive event-Triggered control of nonlinear continuous-Time systems," Applied Mathematics and Computation, Elsevier, vol. 408(C).
    4. Ju, Xinxu & Jia, Xianglei & Shi, Xiaocheng & Yu, Shan’en, 2022. "Adaptive output feedback event-triggered tracking control for nonlinear systems with unknown control coefficient," Applied Mathematics and Computation, Elsevier, vol. 432(C).

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