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Indirect Neural-Enhanced Integral Sliding Mode Control for Finite-Time Fault-Tolerant Attitude Tracking of Spacecraft

Author

Listed:
  • Qijia Yao

    (School of Aerospace Engineering, Beijing Institute of Technology, Beijing 100081, China)

  • Hadi Jahanshahi

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

  • Stelios Bekiros

    (FEMA, University of Malta, MSD 2080 Msida, Malta
    LSE Health, Department of Health Policy, London School of Economics and Political Science, London WC2A 2AE, UK
    IPAG Business School (IPAG), 184 Boulevard Saint-Germain, 75006 Paris, France)

  • Sanda Florentina Mihalache

    (Automatic Control, Computers & Electronics Department, Petroleum-Gas University of Ploiești, 100680 Ploiești, Romania)

  • Naif D. Alotaibi

    (Department of Electrical and Computer Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah 21589, Saudi Arabia)

Abstract

In this article, a neural integral sliding mode control strategy is presented for the finite-time fault-tolerant attitude tracking of rigid spacecraft subject to unknown inertia and disturbances. First, an integral sliding mode controller was developed by originally constructing a novel integral sliding mode surface to avoid the singularity problem. Then, the neural network (NN) was embedded into the integral sliding mode controller to compensate the lumped uncertainty and replace the robust switching term. In this way, the chattering phenomenon was significantly suppressed. Particularly, the mechanism of indirect neural approximation was introduced through inequality relaxation. Benefiting from this design, only a single learning parameter was required to be adjusted online, and the computation burden of the proposed controller was extremely reduced. The stability argument showed that the proposed controller could guarantee that the attitude and angular velocity tracking errors were regulated to the minor residual sets around zero in a finite time. It was noteworthy that the proposed controller was not only strongly robust against unknown inertia and disturbances, but also highly insensitive to actuator faults. Finally, the effectiveness and advantages of the proposed control strategy were validated using simulations and comparisons.

Suggested Citation

  • Qijia Yao & Hadi Jahanshahi & Stelios Bekiros & Sanda Florentina Mihalache & Naif D. Alotaibi, 2022. "Indirect Neural-Enhanced Integral Sliding Mode Control for Finite-Time Fault-Tolerant Attitude Tracking of Spacecraft," Mathematics, MDPI, vol. 10(14), pages 1-18, July.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:14:p:2467-:d:863759
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    References listed on IDEAS

    as
    1. Li, Jun-Feng & Jahanshahi, Hadi & Kacar, Sezgin & Chu, Yu-Ming & Gómez-Aguilar, J.F. & Alotaibi, Naif D. & Alharbi, Khalid H., 2021. "On the variable-order fractional memristor oscillator: Data security applications and synchronization using a type-2 fuzzy disturbance observer-based robust control," Chaos, Solitons & Fractals, Elsevier, vol. 145(C).
    2. Bekiros, Stelios & Jahanshahi, Hadi & Bezzina, Frank & Aly, Ayman A., 2021. "A novel fuzzy mixed H2/H∞ optimal controller for hyperchaotic financial systems," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    3. Yao, Qijia, 2021. "Neural adaptive learning synchronization of second-order uncertain chaotic systems with prescribed performance guarantees," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    4. Wang, Yong-Long & Jahanshahi, Hadi & Bekiros, Stelios & Bezzina, Frank & Chu, Yu-Ming & Aly, Ayman A., 2021. "Deep recurrent neural networks with finite-time terminal sliding mode control for a chaotic fractional-order financial system with market confidence," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    5. Xiong, Pei-Ying & Jahanshahi, Hadi & Alcaraz, Raúl & Chu, Yu-Ming & Gómez-Aguilar, J.F. & Alsaadi, Fawaz E., 2021. "Spectral Entropy Analysis and Synchronization of a Multi-Stable Fractional-Order Chaotic System using a Novel Neural Network-Based Chattering-Free Sliding Mode Technique," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
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    Citations

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    Cited by:

    1. Fawaz E. Alsaadi & Amirreza Yasami & Christos Volos & Stelios Bekiros & Hadi Jahanshahi, 2023. "A New Fuzzy Reinforcement Learning Method for Effective Chemotherapy," Mathematics, MDPI, vol. 11(2), pages 1-25, January.
    2. Hajid Alsubaie & Amin Yousefpour & Ahmed Alotaibi & Naif D. Alotaibi & Hadi Jahanshahi, 2023. "Stabilization of Nonlinear Vibration of a Fractional-Order Arch MEMS Resonator Using a New Disturbance-Observer-Based Finite-Time Sliding Mode Control," Mathematics, MDPI, vol. 11(4), pages 1-14, February.
    3. Bekiros, Stelios & Yao, Qijia & Mou, Jun & Alkhateeb, Abdulhameed F. & Jahanshahi, Hadi, 2023. "Adaptive fixed-time robust control for function projective synchronization of hyperchaotic economic systems with external perturbations," Chaos, Solitons & Fractals, Elsevier, vol. 172(C).
    4. Alsaade, Fawaz W. & Yao, Qijia & Bekiros, Stelios & Al-zahrani, Mohammed S. & Alzahrani, Ali S. & Jahanshahi, Hadi, 2022. "Chaotic attitude synchronization and anti-synchronization of master-slave satellites using a robust fixed-time adaptive controller," Chaos, Solitons & Fractals, Elsevier, vol. 165(P2).
    5. Jahanshahi, Hadi & Yao, Qijia & Alotaibi, Naif D., 2024. "Fixed-time nonsingular adaptive attitude control of spacecraft subject to actuator faults," Chaos, Solitons & Fractals, Elsevier, vol. 179(C).
    6. Nguyen Xuan-Mung & Mehdi Golestani & Sung Kyung Hong, 2023. "Constrained Nonsingular Terminal Sliding Mode Attitude Control for Spacecraft: A Funnel Control Approach," Mathematics, MDPI, vol. 11(1), pages 1-23, January.

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