Ant colony algorithm based fuzzy PID control of unmanned aerial vehicle under wind disturbance conditions

Abstract

A drone is an unmanned aerial vehicle that has been widely used in military, civil and commercial fields. UAVs need to maintain a smooth and stable flight state during flight to accomplish various tasks, such as reconnaissance, scouting, aerial photography, transportation, and so on. In this paper, both the ant colony algorithm and fuzzy PID control are utilized to investigate the control of quadrotor UAVs under wind disturbance conditions. The optimization of the fuzzy PID control algorithm is conducted through the application of a convolutional neural network under wind disturbance conditions.The system construction and simulation test are conducted using MATLAB and Simulink. The experimental results are analyzed, experimental conclusions are drawn, and the results are compared with those obtained using the traditional PID control algorithm and fuzzy PID control algorithm. This comparison helps demonstrate the extent of optimization achieved by the convolutional neural network on the fuzzy PID control algorithm.The results obtained from comparing the performance with the traditional PID control algorithm and fuzzy PID control algorithm demonstrate the degree of optimization achieved by applying the convolutional neural network to the fuzzy PID control algorithm. The findings indicate that the fuzzy PID control, optimized by the ant colony algorithm, can effectively be utilized for controlling quadrotor UAVs under wind disturbance conditions.