Decentralized linear time-varying model predictive control of a formation of unmanned aerial vehicles

This paper proposes a hierarchical MPC approach to stabilization and autonomous navigation of a formation of unmanned aerial vehicles (UAVs), under constraints on motor thrusts, angles and positions, and under collision avoidance constraints. Each vehicle is of quadcopter type and is stabilized by a local linear time-invariant (LTI) MPC controller at the lower level of the control hierarchy around commanded desired set-points. These are generated at the higher level and at a slower sampling rate by a linear time-varying (LTV) MPC controller per vehicle, based on an a simplified dynamical model of the stabilized UAV and a novel algorithm for convex under-approximation of the feasible space. Formation flying is obtained by running the above decentralized scheme in accordance with a leader-follower approach. The performance of the hierarchical control scheme is assessed through simulations, and compared to previous work in which a hybrid MPC scheme is used for planning paths on-line.