Data-Driven MPC for Quadrotors

• Published in: IEEE robotics and automation letters Vol. 6; no. 2; pp. 3769 - 3776
• Main Authors: Torrente, Guillem, Kaufmann, Elia, Fohn, Philipp, Scaramuzza, Davide
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.04.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aerial systems: perception and autonomy; Aerodynamic forces; Aerodynamics; Computational modeling; Control systems; Feedback control; Gaussian process; Gaussian processes; model learning for control; model predictive control; Predictive control; Predictive models; Real time; Rotary wing aircraft; Rotors; Stability; Tracking errors; Trajectory; Vehicle dynamics
• DOI: 10.1109/LRA.2021.3061307
• ISSN: 2377-3766

Aerodynamic forces render accurate high-speed trajectory tracking with quadrotors extremely challenging. These complex aerodynamic effects become a significant disturbance at high speeds, introducing large positional tracking errors, and are extremely difficult to model. To fly at high speeds, feedback control must be able to account for these aerodynamic effects in real-time. This necessitates a modeling procedure that is both accurate and efficient to evaluate. Therefore, we present an approach to model aerodynamic effects using Gaussian Processes, which we incorporate into a Model Predictive Controller to achieve efficient and precise real-time feedback control, leading to up to 70% reduction in trajectory tracking error at high speeds. We verify our method by extensive comparison to a state-of-the-art linear drag model in synthetic and real-world experiments at speeds of up to 14 m/s and accelerations beyond 4 g.