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. 2024 Nov 21;19(11):e0308997.
doi: 10.1371/journal.pone.0308997. eCollection 2024.

An improved trajectory tracking control of quadcopter using a novel Sliding Mode Control with Fuzzy PID Surface

Elisabeth Andarge Gedefaw  1 , Chala Merga Abdissa  1 , Lebsework Negash Lemma  1
Affiliations

An improved trajectory tracking control of quadcopter using a novel Sliding Mode Control with Fuzzy PID Surface

Elisabeth Andarge Gedefaw et al. PLoS One. .

Abstract

This paper presents Super Twisting Sliding Mode Control with a novel Fuzzy PID Surface for improved trajectory tracking of quadrotor unmanned aerial vehicles under external disturbances. First, quadrotor dynamic model with six degrees of freedom (6-DOF) is developed using Newton-Euler Method. Then, a robust Sliding Mode Control based on a new Fuzzy PID Surface is designed to be capable of automatically adjusting its gain parameters. The proposed SMC controller applies super twisting algorithm with PID surface to reduce chattering and a fuzzy logic controller to automatically adjust the gain parameters in order to enhance robustness. Furthermore, the solution to stability has been given by the Lyapunov method. The controller's performance is tested through various trajectories, parameter variations, and disturbance scenarios, comparing it with recent alternatives such as Sliding Mode Control, Fuzzy Sliding Mode Control, and Fuzzy Super Twisting Sliding Mode Control using numerical simulations. The simulation results show that the proposed controller has better tracking performance, parameter variation handling, and disturbance rejection capability compared with the aforementioned controllers. Additionally, the control efforts of the proposed method are minimal and smooth, proving it to be an economically feasible controller and operationally safe for the quadrotor.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Quadcopter configuration.
Fig 2
Fig 2. Throttle.
Fig 3
Fig 3. Roll.
Fig 4
Fig 4. Pitch.
Fig 5
Fig 5. Yaw.
Fig 6
Fig 6. Block diagram of the proposed control strategy.
Fig 7
Fig 7. Trajectory tracking for x-axis in spiral infinity path.
Fig 8
Fig 8. Trajectory tracking for y-axis in spiral infinity path.
Fig 9
Fig 9. Trajectory tracking for z-axis in spiral infinity path.
Fig 10
Fig 10. 3D trajectory tracking of a spiral infinity path.
Fig 11
Fig 11. Trajectory tracking for x-axis in a square wave path.
Fig 12
Fig 12. Trajectory tracking for y-axis in a square wave path.
Fig 13
Fig 13. Trajectory tracking for z-axis in a square wave path.
Fig 14
Fig 14. 3D trajectory tracking of a square wave path.
Fig 15
Fig 15. Trajectory tracking for x-axis in a ramp helix path.
Fig 16
Fig 16. Trajectory tracking for y-axis in a ramp helix path.
Fig 17
Fig 17. Trajectory tracking for z-axis in a ramp helix path.
Fig 18
Fig 18. Trajectory tracking for psi in a ramp helix path.
Fig 19
Fig 19. 3D trajectory tracking of a ramp helix path.
Fig 20
Fig 20. Disturbance rejection capacity of the proposed controller for the x-axis.
Fig 21
Fig 21. Disturbance rejection capacity of the proposed controller for the y-axis.
Fig 22
Fig 22. Disturbance rejection capacity of the proposed controller for the z-axis.
Fig 23
Fig 23. Parameter variation handling capacity of the proposed controller for the x-axis.
Fig 24
Fig 24. Parameter variation handling capacity of the proposed controller for the y-axis.
Fig 25
Fig 25. Parameter variation handling capacity of the proposed controller for the z-axis.
Fig 26
Fig 26. 3D parameter variation handling capability of the proposed controller.
Fig 27
Fig 27. Tracking response for the x-axis.
Fig 28
Fig 28. Tracking response for the y-axis.
Fig 29
Fig 29. Tracking response for the z-axis.
Fig 30
Fig 30. Altitude controller effort of quadcopter.
Fig 31
Fig 31. Attitude controller u2 effort for quadcopter.
Fig 32
Fig 32. Attitude controller u3 effort for quadcopter.
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