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Command filter-based adaptive neural finite-time control for stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation

Author

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  • Yulin Li
  • Ben Niu
  • Guangdeng Zong
  • Jinfeng Zhao
  • Xudong Zhao

Abstract

This article studies a tracking control problem for a class of stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation. Firstly, the Gauss Error Function is introduced to solve the difficulty arising from the saturation nonlinearity. Meanwhile, to overcome the problem of calculating explosion caused by the repeated differentiation of the virtual control signals, a finite-time command filter with a compensation mechanism is developed. Combining the neural networks' approximation ability and the backstepping technique, an adaptive neural finite-time control strategy is proposed for the considered system by constructing the time-varying barrier Lyapunov function. Under the proposed control strategy, it is guaranteed that all signals are bounded in a sense of mean square, the output of the system can track the reference signal within a finite time and all states will not violate the constraints. Furthermore, the stability of the closed-loop system is analysed based on the stochastic finite-time stability theory. Finally, a simulation example verifies the effectiveness of the proposed control strategy.

Suggested Citation

  • Yulin Li & Ben Niu & Guangdeng Zong & Jinfeng Zhao & Xudong Zhao, 2022. "Command filter-based adaptive neural finite-time control for stochastic nonlinear systems with time-varying full-state constraints and asymmetric input saturation," International Journal of Systems Science, Taylor & Francis Journals, vol. 53(1), pages 199-221, January.
    • Handle: RePEc:taf:tsysxx:v:53:y:2022:i:1:p:199-221
    DOI: 10.1080/00207721.2021.1943562
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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. Wojciech Giernacki, 2022. "Minimum Energy Control of Quadrotor UAV: Synthesis and Performance Analysis of Control System with Neurobiologically Inspired Intelligent Controller (BELBIC)," Energies, MDPI, vol. 15(20), pages 1-23, October.
    3. Ahmad Taher Azar & Farah Ayad Abdul-Majeed & Hasan Sh. Majdi & Ibrahim A. Hameed & Nashwa Ahmad Kamal & Anwar Jaafar Mohamad Jawad & Ali Hashim Abbas & Wameedh Riyadh Abdul-Adheem & Ibraheem Kasim Ibr, 2022. "Parameterization of a Novel Nonlinear Estimator for Uncertain SISO Systems with Noise Scenario," Mathematics, MDPI, vol. 10(13), pages 1-17, June.
    4. Weijun Hu & Jiale Quan & Xianlong Ma & Mostafa M. Salah & Ahmed Shaker, 2023. "Analytical Design of Optimal Model Predictive Control and Its Application in Small-Scale Helicopters," Mathematics, MDPI, vol. 11(8), pages 1-15, April.
    5. Guo, Shiyu & Zhao, Xudong & Wang, Huanqing & Xu, Ning, 2023. "Distributed consensus of heterogeneous switched nonlinear multiagent systems with input quantization and DoS attacks," Applied Mathematics and Computation, Elsevier, vol. 456(C).
    6. Zhao, Yanwei & Wang, Huanqing & Xu, Ning & Zong, Guangdeng & Zhao, Xudong, 2023. "Reinforcement learning-based decentralized fault tolerant control for constrained interconnected nonlinear systems," Chaos, Solitons & Fractals, Elsevier, vol. 167(C).

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