A Comparative Study between NMPC and Baseline Feedback Controllers for UAV Trajectory Tracking

Transport, rescue, search, surveillance, and disaster relief tasks are some applications that can be developed with unmanned aerial vehicles (UAVs), where accurate trajectory tracking is a crucial property to operate in cluttered environment or under uncertainties. However, this challenging due high nonlinear dynamics, system constraints, uncertainties presented environments. Hence, the form of unmodeled aerodynamic effects, external disturbances such as wind produce unstable feedback control schemes, introducing significant positional errors. This work presents detailed comparative study between controllers model predictive (NMPC) non-predictive baseline controllers, particular attention accuracy computational efficiency. The development controller schemes was divided into inverse differential kinematics dynamic compensation vehicle. design two uses mathematical UAV theory, guaranteeing low cost an asymptotically stable algorithm. NMPC formulation considering simplified included; additionally, boundaries actions candidate Lyapunov function guarantees stability structure. Finally, commercial simulator DJI brand Matrice 100 real-world experiments, shows reduction error, indicating advantages formulation.