A Novel Approach for Trajectory Tracking of UAVs

Research on unmanned aircraft is improving constantly the autonomous flight capabilities of these vehicles in order to provide performance needed to employ them in even more complex tasks. UAV Path Planning (PP) system plans the best path to perform the mission and then it uploads this path on the Flight Management System (FMS) providing reference to the aircraft navigation. Tracking the path is the way to link kinematic references related to the desired aircraft positions with its dynamic behaviours, to generate the right command sequence. This paper presents a Nonlinear Model Predictive Control (NMPC) system that tracks the reference path provided by PP, solving on-line (i.e. at each sampling time) a finite horizon (state horizon) open loop optimal control problem with Genetic Algorithm (GA). The GA finds the command sequence that minimizes the tracking error with respect to the reference path, driving the aircraft toward the desired trajectory. The same approach is also used to demonstrate the ability of the guidance laws to avoid the collision with static and dynamic obstacles sensed by a stereoscopic optical sensor. The tracking system proposed reflects the merits of NMPC, successfully accomplishing with the tasks. As a matter of fact good tracking performance is evidenced and effective control actions provide smooth and safe paths, both in nominal and off-nominal conditions.