Hierarchical and hybrid model predictive control of quadcopter air vehicles

This paper proposes a hierachical hybrid MPC approach to design feedback control functions for stabilization and autonomous navigation of unmanned air vehicles. After formulating the nonlinear dynamical equations of a "quadcopter" air vehicle, a linear MPC controller is designed to stabilize the vehicle around commanded desired set-points. These are generated at a slower sampling rate by a hybrid MPC controller at the upper control layer, based on a hybrid dynamical model of the UAV and of its surrounding environment, with the overall goal of controlling the vehicle to a target set-point while avoiding obstacles. The performance of the complete hierarchical control scheme is assessed through simulations and visualization in a virtual 3D environment, showing the ability of linear MPC to handle the strong couplings among the dynamical variables of the quadcopter under various torque and angle/position constraints, and the flexibility of hybrid MPC in planning the desired trajectory on-line