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The growing field of aerial manipulation often relies on fully actuated oromnidirectional micro aerial vehicles (OMAVs) which can apply arbitrary forcesand torques while in contact with the environment. Control methods are usuallybased on model-free approaches, separating a high-level wrench controller froman actuator allocation. If necessary, disturbances are rejected by onlinedisturbance observers. However, while being general, this approach oftenproduces sub-optimal control commands and cannot incorporate constraints givenby the platform design. We present two model-based approaches to control OMAVsfor the task of trajectory tracking while rejecting disturbances. The first oneoptimizes wrench commands and compensates model errors by a model learned fromexperimental data. The second one optimizes low-level actuator commands,allowing to exploit an allocation nullspace and to consider constraints givenby the actuator hardware. The efficacy and real-time feasibility of bothapproaches is shown and evaluated in real-world experiments.

MPC with Learned Residual Dynamics with Application on Omnidirectional MAVs