Abstract
Since quadrotor UAVs often need to fly in complex and changing environments, their systems suffer from slow smooth control response, weak self-turbulence capability, and poor self-adaptability. Thus, it is crucially important to carefully formulate a quadrotor UAV control system that can maintain high-precision control and high immunity to disturbance in complex environments. In this paper, an improved nonlinear cascaded fuzzy PID control approach for quadrotor UAVs based on RBF neural network is proposed. Based on the analysis and establishment of the UAV flight control model, this paper designs a control approach with an outer-loop fuzzy adaptive PID control and an inner-loop RBF neural network. The simulation results show that introducing RBF neural networks into the nonlinear fuzzy adaptive PID control can make it have better high-precision control and high anti-disturbance under the influence of different environmental variables.
*Supported by Natural Science Foundation of Chongqing cstc2020jcyj-msxmX0057.
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References
Xiao, C., Mao, Y., Yuan, H., Wen, Y.: Design and simulation of intelligent control algorithm for quadcopter under windy conditions. Comput. Sci. 45(05), 310–316 (2018)
Elmokadem, T., Savkin, A.V.: A method for autonomous collision-free navigation of a quadrotor UAV in unknown tunnel-like environments. Robotica 40(4), 835–861 (2022)
Gao, H., Yan, S.: Research on flight attitude control technology of quadcopter in high-altitude environments. Electron. Des. Eng. 29(15), 17–20,26 (2021)
Jiang, B., Li, B., Zhou, W., Lo, L.Y., Chen, C.K., Wen, C.Y.: Neural network based model predictive control for a quadrotor UAV. Aerospace 9(8), 460 (2022)
Fu, C., Sarabakha, A., Kayacan, E., Wagner, C., John, R., Garibaldi, J.M.: Input uncertainty sensitivity enhanced nonsingleton fuzzy logic controllers for long-term navigation of quadrotor UAVs. IEEE/ASME Trans. Mechatron. 23(2), 725–734 (2018)
Eltayeb, A., Rahmat, M.F.A., Basri, M.A.M., Eltoum, M.M., El-Ferik, S.: An improved design of an adaptive sliding mode controller for chattering attenuation and trajectory tracking of the quadcopter UAV. IEEE Access 8, 205968–205979 (2020)
Shen, Y., Xing, H., Wang, S.: Particle swarm optimization-based cascaded fuzzy PID flight control system for unmanned aerial vehicles. Electron. Measure. Technol. 45(01), 96–103 (2022)
Dong, J., He, B.: Novel fuzzy PID-type iterative learning control for quadrotor UAV. Sensors 19(1), 24 (2018)
Abbasi, E., Mahjoob, M., Yazdanpanah, R.: Controlling of quadrotor UAV using a fuzzy system for tuning the pid gains in hovering mode. In: 10th International Conference on Advances in Computer Entertainment Technology, pp. 1–6. Tehran, Iran (2013)
Wang, Y., Chenxie, Y., Tan, J., Wang, C., Wang, Y., Zhang, Y.: Fuzzy radial basis function neural network PID control system for a quadrotor UAV based on particle swarm optimization. In: 2015 IEEE International Conference on Information and Automation, pp. 2580–2585. Lijiang, China (2015)
Yu, B., Gao, J., Wang, H., Fan, X., Li, X.: Research on RBF neural network attitude control of quadcopter. Mechanical Science and Technology, 1–8 (2023)
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Huang, Z., Wang, H., Wang, X. (2024). Cascaded Fuzzy PID Control for Quadrotor UAVs Based on RBF Neural Networks. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14447. Springer, Singapore. https://doi.org/10.1007/978-981-99-8079-6_40
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DOI: https://doi.org/10.1007/978-981-99-8079-6_40
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