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Type Journal Article
Scope Discipline-based scholarship
Title Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors
Organization Unit
Authors
  • Drew Hanover
  • Philipp Foehn
  • Sihao Sun
  • Elia Kaufmann
  • Davide Scaramuzza
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
Journal Title IEEE Robotics and Automation Letters
Publisher Institute of Electrical and Electronics Engineers
Geographical Reach international
ISSN 2377-3766
Volume 7
Number 2
Page Range 690 - 697
Date 2022
Abstract Text 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 letter, 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.
Digital Object Identifier 10.1109/LRA.2021.3131690
Other Identification Number merlin-id:22177
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