Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors (RAL 2021)

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 work, 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.
Reference
D. Hanover, P. Foehn, S. Sun, E.Kaufmann, D.Scaramuzza
Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors
IEEE Robotics and Automation Letters (RA-L), 2021.
PDF: rpg.ifi.uzh.ch/docs/RAL21_Han...
More on our research in Agile Drone Flight: rpg.ifi.uzh.ch/aggressive_flig...
More on our research in Autonomous Drone Racing: rpg.ifi.uzh.ch/research_drone_...
Affiliations:
D. Hanover, P. Foehn, S. Sun, E.Kaufmann, and D. Scaramuzza are with the Robotics and Perception Group, Dep. of Informatics, University of Zurich, and Dep. of Neuroinformatics, University of Zurich and ETH Zurich, Switzerland
rpg.ifi.uzh.ch/

[@paralinq]

You had me at "deliver ice cold beer"

[@gauthamsai7506]

waiting to use this model with the event-based vision to make it dynamic and autonomous

[@Veer-ss6et]

The control strategy is awesome

[@midasphrygia2425]

This is amazing!

[@VrutikShah]

The ending race track perfomance had me 😮

[@mahmoudbakr568]

Amazing.

[@h_cl]

Impressive.

[@praveenvenkatesh8933]

Incredible

[@RWIKDGR8RANA]

incredible indeed

[@mahmoudbakr568]

What quadrotor are you using? Did you guys develop it yourselves?

[@ricardo_9726]

what about this model in particular necessitates a nonlinear control system? would adaptive control still work with linearization?

[@AnaMaria-ql2rd]

Where can we find the code?

[@shuxinhu6460]

Impressive. Can you share the code?

[@RWIKDGR8RANA]

bana le bhai XD