Design of fuzzy PID control system for quad-rotor UAV based on particle swarm algorithm under wind disturbance conditions
DOI:
https://doi.org/10.61173/gefh0t54Keywords:
Quad-rotor, PSO algorithm, Disturbance model of wind, Parameter tuning, Fuzzy PID controlAbstract
This project proposes an intelligent control method that employs a particle swarm algorithm to optimize the rules of a fuzzy controller. The rules of the fuzzy controller are continuously adjusted based on feedback data on the attitude changes of a quadrotor unmanned aerial vehicle and the enhanced particle swarm algorithm. This enables the controller to learn autonomously, thereby enhancing its performance under various conditions. In addition to optimizing the rules of the fuzzy controller, an analysis and modeling process is conducted for the characteristics of wind speed changes under natural conditions. The resulting model is introduced into the system as environmental noise, thereby improving the controller’s performance under different conditions. Experiments are conducted in the Matlab/Simulink simulation environment to test the performance of the control algorithm. The algorithm’s anti-disturbance capability and control accuracy are compared when facing complex disturbances. This research methodology offers new possibilities for precise control of quadrotor unmanned aerial vehicles and provides valuable references for future drone technology development.
References
Provided the feasibility, this methodology could be Optimization[J]. Electronics Optics & Control, 2022, 29(3):16- implemented in the real-world control of quadrotor 20. unmanned aerial vehicles, with ongoing enhancements [3] Wang S, Zhang J ,Yang L . Wind Disturbance Rejection to the rule optimization process facilitated by the particle Control Law Based on AESO for a Hybrid Tail-Sitter UAV[C]// swarm algorithm. The algorithm’s rapid convergence Technical Committee on Control Theory, Chinese Association speed, the ergodicity, randomness, and sensitivity to initial of Automation,Chinese Association of Automation, Systems
values of the chaotic sequence, coupled with the adaptive Engineering Society of China, 2020:6.DOI:10.26914/ inertia weight method, balance the global and local search c.cnkihy.2020.039490.
capabilities of the particle swarm algorithm. This balance [4] Liangqi, L., Renxiang, B., Wuchen, S., & Xinyu, L. (2019). expedites the convergence speed towards the optimal Ship Track-keeping Control Based on Sliding Mode Variable rule during the optimization process, thereby reducing Structure PID Controller and Particle Swarm Optimization. computational overhead. 2019 6th International Conference on Information Science and In conclusion, this research contributes to the field Control Engineering (ICISCE), 887-891. by offering a comprehensive and efficient approach [5] Zhao, T., Duan, GR. Fully actuated system approach to to quadrotor UAV control, paving the way for future attitude control of flexible spacecraft with nonlinear time-
advancements in this domain. Future work will focus varying inertia. Sci. China Inf. Sci. 65, 212201 (2022). https:// on further refining the rule optimization process and doi.org/10.1007/s11432-021-3349-3
Downloads
Published
Issue
Section
License
Copyright (c) 2024 by the authors.
This work is licensed under a Creative Commons Attribution 4.0 International License.
