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Event-triggered hierarchical learning control of air-breathing hypersonic vehicles with predefined-time convergence

Author

Listed:
  • Guan Wang

    (Harbin Institute of Technology)

  • Hongwei Xia

    (Harbin Institute of Technology)

Abstract

This study delves into an event-triggered hierarchical learning control framework for air-breathing hypersonic vehicles subject to practically constrained actuators. The hierarchical learning mechanism is adeptly integrated into both the control and allocation layers. In the control layer, an emotional deterministic learning control methodology is proposed with predefined-time disturbance observers and filters to attain predefined-time convergence of system tracking and learning, while compensating for lumped disturbances stemming from allocation errors and external disturbances. An intelligent triggered allocation approach is implemented in the allocation layer to distribute the desired control effect to the actuator with fast and low-complexity allocation considering practical actuator characteristics. The proposed control scheme ensures that all signals in the closed-loop system converge to a residual set in close proximity to the origin within a predefined time, whose time constant can be adjusted as desired by the designer. Furthermore, as the system is governed by the event-triggered mechanism, the communication burden can be considerably reduced. The effectiveness of the proposed controller is demonstrated through both theoretical proof and numerical simulations.

Suggested Citation

  • Guan Wang & Hongwei Xia, 2025. "Event-triggered hierarchical learning control of air-breathing hypersonic vehicles with predefined-time convergence," Journal of Intelligent Manufacturing, Springer, vol. 36(1), pages 595-618, January.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:1:d:10.1007_s10845-023-02261-7
    DOI: 10.1007/s10845-023-02261-7
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    References listed on IDEAS

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    1. Hasan Tercan & Philipp Deibert & Tobias Meisen, 2022. "Continual learning of neural networks for quality prediction in production using memory aware synapses and weight transfer," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 283-292, January.
    2. Timo Bänziger & Andreas Kunz & Konrad Wegener, 2020. "Optimizing human–robot task allocation using a simulation tool based on standardized work descriptions," Journal of Intelligent Manufacturing, Springer, vol. 31(7), pages 1635-1648, October.
    3. Isaac Kofi Nti & Adebayo Felix Adekoya & Benjamin Asubam Weyori & Owusu Nyarko-Boateng, 2022. "Applications of artificial intelligence in engineering and manufacturing: a systematic review," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1581-1601, August.
    4. Hanqiao Huang & Chang Luo & Bo Han, 2022. "Prescribed performance fuzzy back-stepping control of a flexible air-breathing hypersonic vehicle subject to input constraints," Journal of Intelligent Manufacturing, Springer, vol. 33(3), pages 853-866, March.
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