1. Performance, Precision, and Payloads: Adaptive Nonlinear MPC for Quadrotors
- Author
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Elia Kaufmann, Philipp Foehn, Sihao Sun, Davide Scaramuzza, Drew Hanover, University of Zurich, and Hanover, Drew
- Subjects
FOS: Computer and information sciences ,2606 Control and Optimization ,Control and Optimization ,1707 Computer Vision and Pattern Recognition ,10009 Department of Informatics ,Computer science ,2210 Mechanical Engineering ,Biomedical Engineering ,2207 Control and Systems Engineering ,2204 Biomedical Engineering ,1702 Artificial Intelligence ,000 Computer science, knowledge & systems ,Tracking error ,Reduction (complexity) ,1709 Human-Computer Interaction ,Computer Science - Robotics ,Artificial Intelligence ,Robustness (computer science) ,Control theory ,1706 Computer Science Applications ,Flexibility (engineering) ,business.industry ,Mechanical Engineering ,Computer Science Applications ,Human-Computer Interaction ,Model predictive control ,Nonlinear system ,Control and Systems Engineering ,Computer Vision and Pattern Recognition ,business ,Robotics (cs.RO) ,Agile software development - Abstract
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., 8 Pages, 6 figures, Accepted RAL 2021
- Published
- 2022