  Robust adaptive model predictive control (RAMPC) is a novel control method
that combines robustness guarantees with respect to unknown parameters and
bounded disturbances into a model predictive control scheme. However, RAMPC has
so far only been developed in theory. The goal of this paper is to apply RAMPC
to a physical quadrotor experiment. To the best of our knowledge this is the
first time that RAMPC has been applied in practice using a state space
formulation. In doing so, we highlight important practical challenges such as
computation of $\lambda$-contractive polytopes and dealing with measurement
noise, and propose modifications to RAMPC so that it can be applied on a
quadrotor. We first simulate quadrotor flight with a direct and a decoupled
control architecture in different scenarios. The scenarios include: (i) an
unknown mass of the quadrotor as a package delivery scenario with wind as a
bounded disturbance; and (ii) all rotor efficiencies drop as a power delivery
problem. We then implement these scenarios on a physical quadrotor and present
the experimental results.
