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Hybrid Backstepping Control of a Quadrotor Using a Radial Basis Function Neural Network

Author

Listed:
  • Muhammad Maaruf

    (Control and Instrumentation Engineering Department, Center for Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Waleed M. Hamanah

    (Interdisciplinary Research Center of Renewable Energy and Power Systems (IRC-REPS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
    Applied Research Center for Metrology, Standards and Testing (ARC-MST), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

  • Mohammad A. Abido

    (Interdisciplinary Research Center of Renewable Energy and Power Systems (IRC-REPS), King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
    Department of Electrical Engineering, College of Engineering and Physics, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
    K.A.CARE Energy Research & Innovation Center, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia)

Abstract

This article presents a hybrid backstepping consisting of two robust controllers utilizing the approximation property of a radial basis function neural network (RBFNN) for a quadrotor with time-varying uncertainties. The quadrotor dynamic system is decoupled into two subsystems: the position and the attitude subsystems. As part of the position subsystem, adaptive RBFNN backstepping control (ANNBC) is developed to eliminate the effects of uncertainties, trace the quadrotor’s position, and provide the desired roll and pitch angles commands for the attitude subsystem. Then, adaptive RBFNN backstepping is integrated with integral fast terminal sliding mode control (ANNBIFTSMC) to track the required Euler angles and improve robustness against external disturbances. The proposed technique is advantageous because the quadrotor states trace the reference states in a short period of time without requiring knowledge of dynamic uncertainties and external disturbances. In addition, because the controller gains are based on the desired trajectories, adaptive algorithms are used to update them online. The stability of a closed loop system is proved by Lyapunov theory. Numerical simulations show acceptable attitude and position tracking performances.

Suggested Citation

  • Muhammad Maaruf & Waleed M. Hamanah & Mohammad A. Abido, 2023. "Hybrid Backstepping Control of a Quadrotor Using a Radial Basis Function Neural Network," Mathematics, MDPI, vol. 11(4), pages 1-19, February.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:4:p:991-:d:1069268
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    References listed on IDEAS

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    1. Nguyen Xuan Mung & Ngoc Phi Nguyen & Dinh Ba Pham & Nhu Ngoc Dao & Sung Kyung Hong, 2022. "Synthesized Landing Strategy for Quadcopter to Land Precisely on a Vertically Moving Apron," Mathematics, MDPI, vol. 10(8), pages 1-14, April.
    2. Jun Wang & Yongqiang Tian & Lanfeng Hua & Kaibo Shi & Shouming Zhong & Shiping Wen, 2023. "New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays," Mathematics, MDPI, vol. 11(3), pages 1-18, January.
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    Cited by:

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