In this paper, we propose and experimentally verify a distributed formation control algorithm for a group of multirotor unmanned aerial vehicles (UAVs). The algorithm brings the whole group of UAVs simultaneously to a prescribed submanifold that determines the formation shape in an asymptotically stable fashion in two- and three-dimensional environments. The complete distributed control framework is implemented with the combination of a fast model predictive control method executed at 50 Hz on low-power computers onboard multirotor UAVs and validated via a series of hardware-in-the-loop simulations and real-robot experiments. The experiments are configured to study the control performance in various formation cases of arbitrary time-varying (e.g., expanding, shrinking, or moving) shapes. In the actual experiments, up to four multirotors have been implemented to form arbitrary triangular, rectangular, and circular shapes drawn by the operator via a human-robot interaction device. We also carry out hardware-in-the-loop simulations using up to six onboard computers to achieve spherical formations and a formation moving through obstacles.

This paper presents a nonlinear control approach for quadrotor Micro Aerial Vehicles (MAVs), which combines a backstepping-like regulator based on the solution of a certain class of global output regulation problems for the rigid body equations on SO(3), a robust controller for the system with bounded disturbances, as well as a trajectory generator using a model predictive control method. The proposed algorithm is endowed with strong convergence properties so that it allows the quadrotor MAVs to reach almost all the desired attitudes. The control approach is implemented on a high-payload-capable quadcopter with unstructured dynamics and unknown disturbances. The performance of our algorithm is demonstrated through a series of experimental evaluations and comparisons with another control method on normal and aggressive trajectory tracking tasks.