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Adaptive Sliding Mode Control of Quadrotor UAV Based on Neural Network

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Proceedings of 2021 Chinese Intelligent Systems Conference

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 804))

Abstract

This paper proposes an adaptive sliding mode control method for quadrotor UAV based on neural network. Utilizing the sliding mode control, this method takes into account the modelling error uncertainties and unknown external disturbances in the actual system, and constructs RBF neural network to approximate the unknown nonlinear dynamics of the system model online. The adaptive law was derived by using Lyapunov theory to estimate the weights of the neural network and unknown parameters of the model, which can verify the stability and superiority of the algorithm. At the same time, compared with the adaptive PD control based on RBF neural network, this method has shorter setting time and less overshoot.

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References

  1. Fei, Q., Zhang, J., Wang, Z., Huang, X.: Sliding mode control with uncertain model for a quadrotor UAV’s automatic visual landing problem. In: Proceedings of 2019 Chinese Intelligent Automation Conference, pp. 226-233. Springer, Singapore (2021). https://doi.org/10.1115/1.4025755

  2. Wang, L., Deng, W., Liu, J., Mei, R.: Adaptive sliding mode trajectory tracking control of quadrotor UAV with unknown control direction. In: Proceedings of the 11th International Conference on Modelling, Identification and Control, pp. 597–607. Springer, Singapore (2020). https://doi.org/10.1007/978-981-15-0474-7_56

  3. Noordin, A., Mohd Basri, M.A., Mohamed, Z., Mat Lazim, I.: Adaptive PID controller using sliding mode control approaches for quadrotor UAV attitude and position stabilization. Arab. J. Sci. Eng. 46(2), 963–981 (2020). https://doi.org/10.1007/s13369-020-04742-w

    Article  Google Scholar 

  4. Dierks, T., Jagannathan, S.: Output feedback control of a quadrotor UAV using neural networks. J. IEEE Trans. Neural Netw. 21, 50–66 (2010). https://doi.org/10.1109/TNN.2009.2034145

  5. Rosales, C., Tosetti, S., Soria, C., Rossomando, F.: Neural adaptive PID control of a quadrotor using EFK. J. IEEE Latin Am. Trans. 16, 2722–2730 (2018). https://doi.org/10.1109/TLA.2018.8795113.

  6. Han, J.H., Feng, Y.M., Peng, F., Dong, W., Sheng, X.J.: Attitude and position control of quadrotor UAV using PD-fuzzy sliding mode control. J. Intell. Robot. Appl. 10464, 563–575 (2017). https://doi.org/10.1007/978-3-319-65298-6_51

    Article  Google Scholar 

  7. Avila, J., Franco, C., Alanis, A.Y., Arana-Daniel, N.: Control of quadrotor using a neural network based PID. In: 2018 IEEE Latin American Conference on Computational Intelligence, pp. 1–6, Gudalajara, Mexico (2018). https://doi.org/10.1109/LA-CCI.2018.8625222.

  8. Nguyen, N.P., Mung, N.X., Thanh, H.L.N.N., Huynh, T.T., Lam, N.T., Hong, S.K.: Adaptive sliding mode control for attitude and altitude system of a quadcopter UAV via neural network. J. IEEE Access 9, 40076–40085 (2021). https://doi.org/10.1109/ACCESS.2021.3064883.

  9. Yang, Y.N., Yan, Y.: Attitude regulation for unmanned quadrotors using adaptive fuzzy gain-scheduling sliding mode control. J. Aerospace Sci. Technol. 54, 208–217 (2016). https://doi.org/10.1016/j.ast.2016.04.005

    Article  Google Scholar 

  10. Barghandan, S., Badamchizadeh, M.A., Jahed-Motlagh, M.R.: Improved adaptive fuzzy sliding mode controller for robust fault tolerant of a Quadrotor. Int. J. Control Autom. Syst. 15(1), 427–441 (2017). https://doi.org/10.1007/s12555-015-0313-7

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. College of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin, Guangxi, 541004, China

    Xiru Wu & Jingtong Jia

Authors
  1. Xiru Wu
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  2. Jingtong Jia
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Editor information

Editors and Affiliations

  1. School of Automatic Science and Electrical Engineering, Beihang University, Beijing, China

    Yingmin Jia

  2. School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, China

    Weicun Zhang

  3. School of Mechanical Engineering and Automation, Beihang University, Beijing, China

    Yongling Fu

  4. Beijing Institute of Precision Mechatronics and Controls, Beijing, China

    Zhiyuan Yu

  5. College of Electrical Engineering and Automation, Fuzhou University, Fuzhou, Fujian, China

    Song Zheng

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Wu, X., Jia, J. (2022). Adaptive Sliding Mode Control of Quadrotor UAV Based on Neural Network. In: Jia, Y., Zhang, W., Fu, Y., Yu, Z., Zheng, S. (eds) Proceedings of 2021 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 804. Springer, Singapore. https://doi.org/10.1007/978-981-16-6324-6_76

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