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Adaptive output-feedback control for a class of stochastic nonlinear systems with unknown control directions and hysteresis input

Author

Listed:
  • Fei Shen
  • Xinjun Wang
  • Xinghui Yin

Abstract

This paper is concerned with an adaptive neural output-feedback control for a class of stochastic nonlinear systems with unknown control directions and hysteresis input. An output-feedback controller is developed for stochastic nonlinear via using radial basis function neural networks (RBFNNs) and adaptive backstepping method. A state observer is designed to estimate the unmeasurable system state signals. Nussbaum gain technique is employed to deal with the unknown control directions. Simultaneously, the backlash-like hysteresis input control in this paper is considered. An adaptive controller is designed to ensure that the output tracking error converges on a small region of the origin. Finally, the control scheme ensures that all signals in the closed-loop systems are semi-global uniformly ultimately bounded. Results of simulation cases are presented to prove the effectivity of the theoretical analysis.

Suggested Citation

  • Fei Shen & Xinjun Wang & Xinghui Yin, 2021. "Adaptive output-feedback control for a class of stochastic nonlinear systems with unknown control directions and hysteresis input," International Journal of Systems Science, Taylor & Francis Journals, vol. 52(3), pages 657-670, February.
    • Handle: RePEc:taf:tsysxx:v:52:y:2021:i:3:p:657-670
    DOI: 10.1080/00207721.2020.1837287
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