Wind Compensation in Trajectory Tracking of a Fixed Wing UAV Using a Nonlinear Model Predictive Controller based on the Particle Swarm Optimization

Abstract

In this study, a nonlinear model predictive controller based on the particle swarm optimization is used for trajectory tracking of a fixed wing UAV and its performance is investigated in the presence of wind compensation and without it. For this purpose, the components of the wind velocity and the UAV states are estimated using a nonlinear filter and then compensated in the proposed controller architecture. Finally, stability of the proposed controller is proved and analyzed. The results show that performance of the UAV flight is effectively improved when the components of the wind velocity are compensated within the controller.