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Adaptive Neural Network-Based Fixed-Time Tracking Controller for Disabilities Exoskeleton Wheelchair Robotic System

Author

Listed:
  • Ayman A. Aly

    (Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
    King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
    Ayman A. Aly and Mai The Vu are the first authors, These authors contributed equally to this work.)

  • Mai The Vu

    (School of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Korea
    Ayman A. Aly and Mai The Vu are the first authors, These authors contributed equally to this work.)

  • Fayez F. M. El-Sousy

    (Department of Electrical Engineering, Prince Sattam Bin Abdulaziz University, Al Kharj 16278, Saudi Arabia)

  • Kuo-Hsien Hsia

    (Department of Electrical Engineering, National Yunlin University of Science and Technology, 123 University Road, Douliou 64002, Taiwan)

  • Ahmed Alotaibi

    (Department of Mechanical Engineering, College of Engineering, Taif University, Taif 21944, Saudi Arabia
    King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia)

  • Ghassan Mousa

    (King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
    Department of Mechanical Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

  • Dac-Nhuong Le

    (King Salman Center for Disability Research, Riyadh 11614, Saudi Arabia
    Institute of Research and Development, Duy Tan University, Danang 550000, Vietnam)

  • Saleh Mobayen

    (Graduate School of Intelligent Data Science, National Yunlin University of Science and Technology, 123 University Road Section 3, Douliou 640301, Taiwan)

Abstract

In this paper, an adaptive neural network approach is developed based on the integral nonsingular terminal sliding mode control method, with the aim of fixed-time position tracking control of a wheelchair upper-limb exoskeleton robot system under external disturbance. The dynamical equation of the upper-limb exoskeleton robot system is obtained using a free and typical model of the robotic manipulator. Afterward, the position tracking error between the actual and desired values of the upper-limb exoskeleton robot system is defined. Then, the integral nonsingular terminal sliding surface based on tracking error is proposed for fixed-time convergence of the tracking error. Furthermore, the adaptive neural network procedure is proposed to compensate for the external disturbance which exists in the upper-limb exoskeleton robotic system. Finally, to demonstrate the effectiveness of the proposed method, simulation results using MATLAB/Simulink are provided.

Suggested Citation

  • Ayman A. Aly & Mai The Vu & Fayez F. M. El-Sousy & Kuo-Hsien Hsia & Ahmed Alotaibi & Ghassan Mousa & Dac-Nhuong Le & Saleh Mobayen, 2022. "Adaptive Neural Network-Based Fixed-Time Tracking Controller for Disabilities Exoskeleton Wheelchair Robotic System," Mathematics, MDPI, vol. 10(20), pages 1-17, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3853-:d:945224
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