Adaptive model predictive control for set-point tracking of a single-DOF flapping-wing unmanned aerial vehicle

An adaptive model predictive control (MPC) is proposed in this paper to stabilize linear constrained systems with parametric uncertainties. The proposed control is designed by combining linear MPC and an adaptive updating law for uncertain parameters. It is proved that, by using the proposed adaptive MPC, states of the linear system can be stabilized, and the estimated parameters are bounded. The adaptive MPC can be applied to set-point tracking of a single-DOF flapping-wing unmanned aerial vehicle (UAV), where there exist parametric uncertainties and control constraints. Performances of the closed-loop system are illustrated by a simulation example.