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. The UAV Path Planner (PP) plans the best path to perform the mission. This is a waypoint sequence that is uploaded on the Flight Management System (FMS) providing reference to the aircraft Guidance Navigation and Control System (GNCS). The UAV GNCS converts the waypoint sequence in guidance references for the Flight Control System (FCS) that in turn generates the command sequence needed to track the optimum path. A new Guidance System (GS) is presented in this paper, based on the graph search algorithm Kinematic A* (KA*). The GS is linked to a Nonlinear Model Predictive Control (NMPC) system that tracks the reference path, 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. The tracking system proposed reflects the merits of NMPC, successfully accomplishing with the task. 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.