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. 2024 Oct 10;19(10):e0309098.
doi: 10.1371/journal.pone.0309098. eCollection 2024.

An improved nonsingular adaptive super twisting sliding mode controller for quadcopter

Nardos Belay Abera  1 , Chala Merga Abdissa  1 , Lebsework Negash Lemma  1
Affiliations

An improved nonsingular adaptive super twisting sliding mode controller for quadcopter

Nardos Belay Abera et al. PLoS One. .

Abstract

This paper presents an improved nonsingular adaptive super twisting sliding mode control for tracking of a quadrotor system in the presence of external disturbances and uncertainty. The initial step involves developing a dynamic model for the quadrotor that is free from singularities, achieved through the utilization of the Newton-Quaternion formalism. Then, the super twisting algorithm is used to develop a novel sliding mode control that mitigates chattering. Particle Swarm Optimization (PSO) is employed for the adjustment of the controller gains. Moreover, to maintain stable control of the quadcopter, even in scenarios where the upper limit of disturbances is unknown, an adaptive rule grounded in Lyapunov stability is applied. Simulation results demonstrate that the proposed controller reduces tracking errors to 0.1% for roll, 0.05% for pitch, and 2.2% for altitude, outperforming other state-of-the-art sliding mode controllers. Additionally, the proposed controller effectively rejects disturbances, maintaining minimal steady-state errors of 0.01° for roll, 0.02° for pitch, and 0.001° for yaw, significantly better than conventional controllers. These results highlight tracking and disturbance rejection capabilities of the proposed controller, making its real-time implementation for quadrotor Unmanned Aerial Vehicles (UAVs) feasible.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Alignment of quadcopter reference frames, showing the alignment between body and inertial frames.
Fig 2
Fig 2. Controller architecture for quadrotor, including position, altitude, and virtual control input designs.
Fig 3
Fig 3. Diagram of position controller for maintaining quadrotor trajectory along x, y, and z axes.
Fig 4
Fig 4. Altitude controller diagram for maintaining desired quadrotor altitude.
Fig 5
Fig 5. Flowchart of PSO algorithm used for tuning quadrotor sliding surface parameters.
Fig 6
Fig 6. Comparison of roll angle tracking performance between the proposed controller and other controllers.
Fig 7
Fig 7. Pitch angle tracking performance, highlighting the tracking response of the proposed controller compared to other controllers.
Fig 8
Fig 8. Yaw angle tracking performance comparison, showing similar tracking performance.
Fig 9
Fig 9. Altitude tracking performance: The proposed adaptive controller shows minimal deviation.
Fig 10
Fig 10. Roll angle performance under disturbances, demonstrating the high disturbance rejection of the proposed controller.
Fig 11
Fig 11. Pitch angle performance, highlighting high disturbance rejection and minimal steady state error of proposed controller.
Fig 12
Fig 12. Yaw angle performance under disturbances, highlighting the minimal oscillation of the proposed controller.
Fig 13
Fig 13. Altitude performance under disturbances, showcasing the high disturbance rejection of the proposed controller.
Fig 14
Fig 14. Control effort U1 for altitude control, demonstrating efficient use of control inputs.
Fig 15
Fig 15. Control effort U2 for roll motion control, showing minimal and smooth control by the proposed controller.
Fig 16
Fig 16. Control effort U3 for pitch control, showing minimal and smooth effort by the proposed controller.
Fig 17
Fig 17. Control effort U4 for yaw control, highlighting minimal effort by the proposed controller.
See this image and copyright information in PMC

References

    1. Chen K, Ye C, Wu C, Wang H, Jin L, Zhu F, et al.. A novel open-closed-loop control strategy for quadrotor trajectory tracking on real-time control and acquisition platform. Appl Sci. 2023. Mar;13(5):3251. doi: 10.3390/app13053251 - DOI
    1. Okyere E, Bousbaine A, Poyi GT, Joseph AK, Andrade JM. LQR controller design for quad-rotor helicopters. J Eng. 2019. Jun;2019(17):4003–4007. doi: 10.1049/joe.2018.8126 - DOI
    1. Reddy Maddikunta PK, Hakak S, Alazab M, Bhattacharya S, Gadekallu TR, Khan WZ, et al.. Unmanned aerial vehicles in smart agriculture: Applications, requirements, and challenges. IEEE Sensors J. 2021. Aug;21(16):17608–17619. doi: 10.1109/JSEN.2021.3049471 - DOI
    1. Eltayeb A, Rahmat MF, Basri MAM, Eltoum MAM, Mahmoud MS. Integral adaptive sliding mode control for quadcopter UAV under variable payload and disturbance. IEEE Access. 2022;10:94754–94764. doi: 10.1109/ACCESS.2022.3203058 - DOI
    1. Nguyen NP, Mung NX, Thanh HLNN, Huynh TT, Lam NT, Hong SK. Adaptive sliding mode control for attitude and altitude system of a quadcopter UAV via neural network. IEEE Access. 2021;9:40076–40085. doi: 10.1109/ACCESS.2021.3064883 - DOI

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