  Many multirotor Unmanned Aerial Systems applications have a critical need for
precise position control in environments with strong dynamic external
disturbances such as wind gusts or ground and wall effects. Moreover, to
maximize flight time, small multirotor platforms have to operate within strict
constraints on payload and thus computational performance. In this paper, we
present the design and experimental comparison of Model Predictive and PID
multirotor position controllers augmented with a disturbance estimator to
reject strong wind gusts up to 12 m/s and ground effect. For disturbance
estimation, we compare Extended and Unscented Kalman filtering. In extensive
in- and outdoor flight tests, we evaluate the suitability of the developed
control and estimation algorithms to run on a computationally constrained
platform. This allows to draw a conclusion on whether potential performance
improvements justify the increased computational complexity of MPC for
multirotor position control and UKF for disturbance estimation.
