Experimental verification of the model predictive control with disturbance rejection for quadrotors

In this paper, we describe a model predictive control (MPC) for small unmanned aerial vehicles (UAVs) like the AR. Drone. By storing the disturbance information, we share it to other UAVs on the same path so that the other UAVs can know the disturbance in advance. Moreover, other UAVs are possible to use the disturbance information in the prediction steps. In addition, the prior disturbance information can be included in the model, and we use the model predictive control to perform the optimization calculation. As the result, the UAVs can prepare to the disturbance before it affects the UAVs, which make them possible to improve the stability of the flight. We verify this approach through the simulation and experiment.