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Control of a Hydraulic Generator Regulating System Using Chebyshev-Neural-Network-Based Non-Singular Fast Terminal Sliding Mode Method

Author

Listed:
  • Fawaz E. Alsaadi

    (Communication Systems and Networks Research Group, Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Amirreza Yasami

    (Department of Mechanical Engineering, University of Alberta, Edmonton, AB T6G 1H9, Canada)

  • Hajid Alsubaie

    (Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Ahmed Alotaibi

    (Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia)

  • Hadi Jahanshahi

    (Department of Mechanical Engineering, University of Manitoba, Winnipeg, MB R3T 5V6, Canada)

Abstract

A hydraulic generator regulating system with electrical, mechanical, and hydraulic constitution is a complex nonlinear system, which is analyzed in this research. In the present study, the dynamical behavior of this system is investigated. Afterward, the input/output feedback linearization theory is exerted to derive the controllable model of the system. Then, the chaotic behavior of the system is controlled using a robust controller that uses a Chebyshev neural network as a disturbance observer in combination with a non-singular robust terminal sliding mode control method. Moreover, the convergence of the system response to the desired output in the presence of uncertainty and unexpected disturbances is demonstrated through the Lyapunov stability theorem. Finally, the effectiveness and appropriate performance of the proposed control scheme in terms of robustness against uncertainty and unexpected disturbances are demonstrated through numerical simulations.

Suggested Citation

  • Fawaz E. Alsaadi & Amirreza Yasami & Hajid Alsubaie & Ahmed Alotaibi & Hadi Jahanshahi, 2022. "Control of a Hydraulic Generator Regulating System Using Chebyshev-Neural-Network-Based Non-Singular Fast Terminal Sliding Mode Method," Mathematics, MDPI, vol. 11(1), pages 1-18, December.
    • Handle: RePEc:gam:jmathe:v:11:y:2022:i:1:p:168-:d:1018571
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    References listed on IDEAS

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    1. Qijia Yao & Hadi Jahanshahi & Larissa M. Batrancea & Naif D. Alotaibi & Mircea-Iosif Rus, 2022. "Fixed-Time Output-Constrained Synchronization of Unknown Chaotic Financial Systems Using Neural Learning," Mathematics, MDPI, vol. 10(19), pages 1-14, October.
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