  Agile quadrotor flight in challenging environments has the potential to
revolutionize shipping, transportation, and search and rescue applications.
Nonlinear model predictive control (NMPC) has recently shown promising results
for agile quadrotor control, but relies on highly accurate models for maximum
performance. Hence, model uncertainties in the form of unmodeled complex
aerodynamic effects, varying payloads and parameter mismatch will degrade
overall system performance. In this paper, we propose L1-NMPC, a novel hybrid
adaptive NMPC to learn model uncertainties online and immediately compensate
for them, drastically improving performance over the non-adaptive baseline with
minimal computational overhead. Our proposed architecture generalizes to many
different environments from which we evaluate wind, unknown payloads, and
highly agile flight conditions. The proposed method demonstrates immense
flexibility and robustness, with more than 90% tracking error reduction over
non-adaptive NMPC under large unknown disturbances and without any gain tuning.
In addition, the same controller with identical gains can accurately fly highly
agile racing trajectories exhibiting top speeds of 70 km/h, offering tracking
performance improvements of around 50% relative to the non-adaptive NMPC
baseline.
