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Adaptive Tracking Control for Stochastic Nonlinear Systems with Full-State Constraints and Unknown Covariance Noise

Author

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  • Min, Huifang
  • Xu, Shengyuan
  • Yu, Xin
  • Fei, Shumin
  • Cui, Guozeng

Abstract

This paper is devoted to the adaptive state-feedback tracking control for stochastic nonlinear systems disturbed by unknown covariance noise under the condition of full-state constraints and parametric uncertainties. Different from the related literatures, nonlinear functions in the diffusion terms are allowed to be unknown in this paper. The parametric uncertainties and unknown covariance noise are compensated with the aid of adaptive control design. By combining the backstepping technique with barrier Lyapunov function (BLF) in a unified framework, the full-state constraints can be dealt. Then, an adaptive state-feedback controller is constructed, which guarantees all the signals in the closed-loop system are uniformly ultimately bounded, the system states remain in the defined compact sets and the output tracks the reference signal well. Finally, stochastic noise is introduced to establish a stochastic simple pendulum system to show the effectiveness of the proposed controller.

Suggested Citation

  • Min, Huifang & Xu, Shengyuan & Yu, Xin & Fei, Shumin & Cui, Guozeng, 2020. "Adaptive Tracking Control for Stochastic Nonlinear Systems with Full-State Constraints and Unknown Covariance Noise," Applied Mathematics and Computation, Elsevier, vol. 385(C).
    • Handle: RePEc:eee:apmaco:v:385:y:2020:i:c:s0096300320303520
    DOI: 10.1016/j.amc.2020.125397
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    References listed on IDEAS

    as
    1. Li, Junmin & Yue, Hongyun, 2015. "Adaptive fuzzy tracking control for stochastic nonlinear systems with unknown time-varying delays," Applied Mathematics and Computation, Elsevier, vol. 256(C), pages 514-528.
    2. Shaocheng Tong & Yinyin Xu & Yongming Li, 2015. "Adaptive fuzzy decentralised control for stochastic nonlinear large-scale systems in pure-feedback form," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(8), pages 1510-1524, June.
    3. Li, Zhi-Min & Chang, Xiao-Heng & Yu, Lu, 2016. "Robust quantized H∞ filtering for discrete-time uncertain systems with packet dropouts," Applied Mathematics and Computation, Elsevier, vol. 275(C), pages 361-371.
    4. Zhang, Jing & Xia, Jianwei & Sun, Wei & Zhuang, Guangming & Wang, Zhen, 2018. "Finite-time tracking control for stochastic nonlinear systems with full state constraints," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 207-220.
    5. Xiao, Wenbin & Cao, Liang & Dong, Guowei & Zhou, Qi, 2019. "Adaptive fuzzy control for pure-feedback systems with full state constraints and unknown nonlinear dead zone," Applied Mathematics and Computation, Elsevier, vol. 343(C), pages 354-371.
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    Cited by:

    1. Wang, Sanxia & Xia, Jianwei & Wang, Xueliang & Yang, Wenjing & Wang, Linqi, 2021. "Adaptive neural networks control for MIMO nonlinear systems with unmeasured states and unmodeled dynamics," Applied Mathematics and Computation, Elsevier, vol. 408(C).
    2. Li, Huijuan & Li, Wuquan & Gu, Jianzhong, 2022. "Decentralized stabilization of large-scale stochastic nonlinear systems with time-varying powers," Applied Mathematics and Computation, Elsevier, vol. 418(C).
    3. Yao, Yangang & Tan, Jieqing & Wu, Jian & Zhang, Xu & He, Lei, 2022. "Prescribed tracking error fixed-time control of stochastic nonlinear systems," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    4. Yuan, Manman & Zhai, Junyong & Ye, Hui, 2022. "Adaptive output feedback control for a class of switched stochastic nonlinear systems via an event-triggered strategy," Applied Mathematics and Computation, Elsevier, vol. 422(C).

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