Research on Route Tracking Controller of Quadrotor UAV Based on Fuzzy Logic and RBF Neural Network
Kejin Jia, Siqi Lin, Yun Du, Chao Zou, Mengyanglin Lu
Abstract
A proportional integral differential (PID) trajectory tracking control strategy based on fuzzy logic and RBF neural network is proposed for the trajectory stability tracking control of the quadrotor unmanned aerial vehicle (UAV) control system. Firstly, the trajectory tracking problem of UAV is transformed into the command tracking control problem of PID position control loop and PID attitude control loop by transformation. Then, the fuzzy control theory is used to adjust the PID parameter gain adaptively in real time, so as to overcome the shortcomings of the traditional PID parameters relying on experience and unable to adjust in real time according to the change of the system. At the same time, the online compensator of PID parameter gain is designed by using the attention mechanism of radial basis function (RBF) neural network, and the disturbance caused by the environmental impact of the system is suppressed by online learning and adjustment. Finally, the designed controller (RFPID) is compared with the PID controller and the fuzzy-PID (FPID) controller in three simulation environments. The experimental results show that the proposed controller can significantly improve the robustness and accuracy of the trajectory tracking control of the quadrotor UAV.