Adaptive nonlinear model predictive path-following control for a fixed-wing unmanned aerial vehicle

摘要

This paper presents an adaptive Nonlinear Model Predictive Control (NMPC) for the path-following control of a fixed-wing unmanned aerial vehicle (UAV). The objective is to minimize the mean and maximum errors between the reference path and the UAV. Navigating in a cluttered environment requires accurate tracking. However, linear controllers cannot provide good tracking performance due to nonlinearities that arise in the system dynamics and physical limitations such as actuator saturation and state constraints. NMPC provides an alternative since it can combine multiple objectives and constraints, which minimize the objective function. However, it is difficult to decide appropriate control horizon since the path-following performance depends on the profile of the path. Therefore, a fixed-horizon NMPC cannot guarantee accurate tracking performance. An adaptive NMPC that varies the control horizon according to the path curvature profile for tight tracking is proposed in this paper. Simulation results show that the proposed adaptive NMPC controller can follow the path more accurately than a conventional, fixed-horizon NMPC.