A flight control system for quadrotor plant protection UAV based on RBF-PID algorithm

Abstract

As a powerful tool for modern agricultural protection, unmanned aerial vehicle (UAV) has the advantages of good adaptability and high efficiency in spraying and other operations compared with traditional manual operations The attitude and control of plant protection UAV are greatly affected by external factors during operation. However, the traditional proportional-integral-derivative (PID) controller commonly used at present cannot adapt to the complex environment because of the fixed parametersTherefore, a more sensitive method is needed to achieve adaptive balance of the aircraft attitude. In this paper, we establish the dynamic mathematical model of quadrotor UAV, and the influence of air resistance and rotational torque is considered in the process. Adopting the combined control method of Radial Basis Function (RBF) neural network and PID, the dynamic tuning of system control parameters is achieved through the self-learning of neural network and the feature of nonlinear mapping. The control system not only has the advantages of simple PID control structure and clear physical meaning, but also has the ability of neural network self-learning and adaptation. Matlab/Simulink was used as the experimental platform to respectively simulate the RBF-PID control system and the traditional PID control system. The simulation results show that the control system based on RBF-PID algorithm has a shorter response time and stronger robustness, which enhances the systems self-adaptability