Data-Driven MPC for Quadrotors

By Guillem Torrente, Elia Kaufmann, Philip Föhn, Davide Scaramuzza. IEEE Robotics and Automation Letters, 2021

304 citations recorded by the source corpus.

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.

Subfields: Control and Systems Engineering, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/809ab90454505f513dff93e2d8b440940eeb64aa

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

In Statistical and Nonlinear Physics

Citations in the corpus

Neural-Fly enables rapid learning for agile flight in strong winds (2022, methodology): “[11] leverages an aerodynamic model learned offline and represented as Gaussian Processes.”