Robust Adaptive Model Predictive Control of Quadrotors

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.