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Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural Network

Author

Listed:
  • Jinjun Rao

    (Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Bo Li

    (Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Zhen Zhang

    (Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Dongdong Chen

    (Shanghai Key Laboratory of Intelligent Manufacturing and Robotics, School of Mechatronic Engineering and Automation, Shanghai University, Shanghai 200444, China)

  • Wojciech Giernacki

    (Faculty of Control, Institute of Robotics and Machine Intelligence, Robotics and Electrical Engineering, Poznan University of Technology, Piotrowo 3A, 60-965 Poznan, Poland)

Abstract

In this article, a cascade fuzzy neural network (FNN) control approach is proposed for position control of quadrotor unmanned aerial vehicle (UAV) system with high coupling and underactuated. For the attitude loop with limited range, the FNN controller parameters were trained offline using flight data, whereas for the position loop, the method based on FNN compensation proportional-integral-derivative (PID) was adopted to tune the system online adaptively. This method not only combined the advantages of fuzzy systems and neural networks but also reduced the amount of calculation for cascade neural network control. Simulations of fixed set point flight and spiral and square trajectory tracking flight were then conducted. The comparison of the results showed that our method had advantages in terms of minimizing overshoot and settling time. Finally, flight experiments were carried out on a DJI Tello quadrotor UAV. The experimental results showed that the proposed controller had good performance in position control.

Suggested Citation

  • Jinjun Rao & Bo Li & Zhen Zhang & Dongdong Chen & Wojciech Giernacki, 2022. "Position Control of Quadrotor UAV Based on Cascade Fuzzy Neural Network," Energies, MDPI, vol. 15(5), pages 1-18, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1763-:d:759812
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    Citations

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    Cited by:

    1. Nana Cheng & Chaoli Wang, 2023. "A Switching Mode Control Scheme for the Hovering Control of Quadrotor Unmanned Aerial Vehicles," Mathematics, MDPI, vol. 11(4), pages 1-28, February.
    2. Wojciech Giernacki & Sławomir Stępień & Marcin Chodnicki & Agnieszka Wróblewska, 2022. "Hybrid Quasi-Optimal PID-SDRE Quadrotor Control," Energies, MDPI, vol. 15(12), pages 1-13, June.

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