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Adaptive finite-time neural network control for nonlinear stochastic systems with state constraints

Author

Listed:
  • Liu, Xiaohua
  • Li, Mengling
  • Zeng, Pengyu

Abstract

This paper focuses on the adaptive finite-time neural network control problem for nonlinear stochastic systems with full state constraints. Adaptive controller and adaptive law are designed by backstepping design with log-type barrier Lyapunov function. Radial basis function neural networks are employed to approximate unknown system parameters. It is proved that the tracking error can achieve finite-time convergence to a small region of the origin in probability and the state constraints are confirmed in probability. Different from deterministic nonlinear systems, here the stochastic system is affected by two random terms including continuous Brownian motion and discontinuous Poisson jump process. Therefore, it will bring difficulties to the controller design and the estimations of unknown parameters. A simulation example is given to illustrate the effectiveness of the designed control method.

Suggested Citation

  • Liu, Xiaohua & Li, Mengling & Zeng, Pengyu, 2024. "Adaptive finite-time neural network control for nonlinear stochastic systems with state constraints," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 215(C), pages 82-97.
    • Handle: RePEc:eee:matcom:v:215:y:2024:i:c:p:82-97
    DOI: 10.1016/j.matcom.2023.08.004
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    References listed on IDEAS

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    1. Berrhazi, Badr-eddine & El Fatini, Mohamed & Laaribi, Aziz & Pettersson, Roger & Taki, Regragui, 2017. "A stochastic SIRS epidemic model incorporating media coverage and driven by Lévy noise," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 60-68.
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