  Quadrotor flight is an extremely challenging problem due to the limited
control authority encountered at the limit of handling. Model Predictive
Contouring Control (MPCC) has emerged as a promising model-based approach for
time optimization problems such as drone racing. However, the standard MPCC
formulation used in quadrotor racing introduces the notion of the gates
directly in the cost function, creating a multi objective optimization that
continuously trades off between maximizing progress and tracking the path
accurately. This paper introduces three key components that enhance the
state-of-the-art MPCC approach for drone racing. First and foremost, we provide
safety guarantees in the form of a track constraint and terminal set. The track
constraint is designed as a spatial constraint which prevents gate collisions
while allowing for time optimization only in the cost function. Second, we
augment the existing first principles dynamics with a residual term that
captures complex aerodynamic effects and thrust forces learned directly from
real-world data. Third, we use Trust Region Bayesian Optimization (TuRBO), a
state-of-the-art global Bayesian Optimization algorithm, to tune the
hyperparameters of the MPCC controller given a sparse reward based on lap time
minimization. The proposed approach achieves similar lap times to the
best-performing RL policy and outperforms the best model-based controller while
satisfying constraints. In both simulation and real world, our approach
consistently prevents gate crashes with 100% success rate, while pushing the
quadrotor to its physical limits reaching speeds of more than 80km/h.
