Data-Driven MPC for Quadrotors
This addresses the challenge of precise control for quadrotors in high-speed applications, representing a strong specific gain in robotics.
The paper tackled the problem of high-speed trajectory tracking for quadrotors by modeling aerodynamic effects with Gaussian Processes and integrating them into a Model Predictive Controller, resulting in up to a 70% reduction in tracking error at speeds up to 14m/s.
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 modelling 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 14m/s and accelerations beyond 4g.