A Robust MPC-based autopilot for mini UAVs

Unmanned Aerial Vehicles (UAVs) are systems subject to external disturbances and parametric uncertainties. A robust Model Predictive Control (MPC) is proposed as autopilot controller candidate, due to its ability to handle both parametric uncertainties and additive noise. The navigation outer loop (heading variation) is regulated via PID control. The key features of the proposed technique are: (i) the control gain matrix is evaluated offline to guarantee the real-time feasibility of the MPC, (ii) the controller is robust to parametric model uncertainties (i.e. mass and inertia variations) and to random bounded noise (i.e. gust). The design scheme of a customized autopilot is illustrated and different aircraft configurations (in terms of mass, inertia and airspeed variations) are analyzed to validate the presented approach, both for the linear and nonlinear case. Moreover, the performance of the controller system are compared with an L1 adaptive controller.