Model-In-the-Loop Testing of Control Systems and Path Planner Algorithms for QuadRotor UAVs

Real systems, as Unmanned Aerial Vehicles (UAVs), are usually subject to disturbances and parametric uncertainties, which could compromise the mission accomplishment, considering particularly harsh environments or challenging applications. For this reason, the main idea proposed in this research is the design of the on-board software, as autopilot software candidate, for a multirotor UAV. In detail, the inner loop of the autopilot system is designed with a variable structure control system, based on sliding mode theory, able to handle external disturbances and uncertainties. This controller is compared with a simple Proportional-Integral-Derivative controller. The key aspects of the proposed methodology are the robustness to bounded disturbances and parametric uncertainties of the proposed combination of guidance and control algorithms. A path-following algorithm is designated for the guidance task, which provides the desired waypoints to the control algorithm. Model-in-the-loop simulations have been performed to validate the proposed approaches. Computationally efficient algorithms are proposed, as combination of a robust control system and path planner. Extensive simulations are performed to show the effectiveness of the proposed methodologies, considering both disturbances and uncertainties.