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Adaptive Terminal Sliding Mode Control for a Quadrotor System with Barrier Function Switching Law

Author

Listed:
  • Jiangting Zhu

    (College of Locomotive and Rolling Stock Engineering, Guangzhou Railway Polytechnic, Guangzhou 511370, China)

  • Xionghui Long

    (College of Locomotive and Rolling Stock Engineering, Guangzhou Railway Polytechnic, Guangzhou 511370, China)

  • Quan Yuan

    (College of Locomotive and Rolling Stock Engineering, Guangzhou Railway Polytechnic, Guangzhou 511370, China)

Abstract

This study presents a novel finite-time robust control framework for quadrotor systems subjected to model uncertainties and unknown external disturbances. A fast terminal sliding mode (FTSM) manifold is first constructed to achieve finite-time convergence of tracking errors. To address the challenges posed by uncertain system dynamics, a radial basis function neural network (RBFNN) is integrated for real-time approximation of unknown nonlinearities. In addition, an adaptive gain regulation mechanism based on a barrier Lyapunov function (BLF) is developed to ensure boundedness of system trajectories while enhancing robustness without requiring prior knowledge of disturbance bounds. The proposed control scheme guarantees finite-time stability, strong robustness, and precise trajectory tracking. Numerical simulations substantiate the efficacy and superiority of the proposed method in comparison with existing control approaches.

Suggested Citation

  • Jiangting Zhu & Xionghui Long & Quan Yuan, 2025. "Adaptive Terminal Sliding Mode Control for a Quadrotor System with Barrier Function Switching Law," Mathematics, MDPI, vol. 13(8), pages 1-16, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:8:p:1344-:d:1638374
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    References listed on IDEAS

    as
    1. Guangshi Li & Qiuye Sun, 2021. "Adaptive Sliding Mode Control for a Class of Manipulator Systems with Output Constraint," Complexity, Hindawi, vol. 2021, pages 1-7, February.
    2. Longbin Fu & Liwei An & Lili Zhang, 2024. "Attitude-position obstacle avoidance of trajectory tracking control for a quadrotor UAV using barrier functions," International Journal of Systems Science, Taylor & Francis Journals, vol. 55(16), pages 3337-3354, December.
    3. Kamal Elyaalaoui & Moussa Labbadi & Sahbi Boubaker & Souad Kamel & Faisal S. Alsubaei, 2023. "On Novel Fractional-Order Trajectory Tracking Control of Quadrotors: A Predefined-Time Guarantee Performance Approach," Mathematics, MDPI, vol. 11(16), pages 1-18, August.
    4. Kai Zhou & Chengxiang Yuan & Dongyang Sun & Ningzhi Jin & Xiaogang Wu, 2021. "Parameter adaptive terminal sliding mode control for Full-Bridge DC-DC converter," PLOS ONE, Public Library of Science, vol. 16(2), pages 1-14, February.
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    Cited by:

    1. Rui Liu & Jianhua Huang & Biao Lu & Weili Ding, 2025. "Certified Neural Network Control Architectures: Methodological Advances in Stability, Robustness, and Cross-Domain Applications," Mathematics, MDPI, vol. 13(10), pages 1-45, May.

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