Decentralized hybrid model predictive control of a formation of unmanned aerial vehicles

This paper proposes a hierarchical MPC strategy for autonomous navigation of a formation of unmanned aerial vehicles (UAVs) of quadcopter type under obstacle and collision avoidance constraints. Each vehicle is stabilized by a lower-level local linear MPC controller around a desired position, that is generated, at a slower sampling rate, by a hybrid MPC controller per vehicle. Such an upper control layer is based on a hybrid dynamical model of the UAV in closed-loop with its linear MPC controller and of its surrounding environment (i.e., the other UAVs and obstacles). The resulting decentralized scheme controls the formation based on a leader-follower approach. The performance of the hierarchical control scheme is assessed through simulations and comparisons with other path planning strategies, showing the ability of linear MPC to handle the strong couplings among the dynamical variables of each quadcopter under motor voltage and angle/position constraints, and the flexibility of the decentralized hybrid MPC scheme in planning the desired paths on-line