Differential Flatness of Quadrotor Dynamics Subject to Rotor Drag for Accurate Tracking of High-Speed Trajectories
By Matthias Faessler, Antonio Franchi, Davide Scaramuzza. 2017
403 citations recorded by the source corpus.
In this letter, we prove that the dynamical model of a quadrotor subject to linear rotor drag effects is differentially flat in its position and heading. We use this property to compute feedforward control terms directly from a reference trajectory to be tracked. The obtained feedforward terms are then used in a cascaded, nonlinear feedback control law that enables accurate agile flight with quadrotors. Compared to the state-of-the-art control methods, which treat the rotor drag as an unknown disturbance, our method reduces the trajectory tracking error significantly. Finally, we present a method based on a gradient-free optimization to identify the rotor drag coefficients, which are required to compute the feedforward control terms. The new theoretical results are thoroughly validated trough extensive comparative experiments.
Subfields: Aerospace Engineering, Control and Systems Engineering, Mechanical Engineering, Robotics & Control
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Aerospace Engineering
- Davide Scaramuzza
- Jeffrey A. Delmerico
- Roland Yves Siegwart
- Antonio Loquercio
- Marco Hutter
- Davide Falanga
- César Cadena
- Mattia Segu
In Control and Systems Engineering
- Davide Scaramuzza
- Marco Hutter
- Antonio Loquercio
- Roland Yves Siegwart
- Davide Falanga
- Mattia Segu
- Kevin Kleber
- Yisong Yue
In Mechanical Engineering
- Mattia Segu
- Antonio Loquercio
- Marco Hutter
- Roland Yves Siegwart
- Dario Floreano
- Anima Anandkumar
- Davide Scaramuzza
- Soon-Jo Chung
In Robotics & Control
- Antonio Loquercio
- Mattia Segu
- Davide Scaramuzza
- Marco Hutter
- Roland Yves Siegwart
- Yisong Yue
- Dario Floreano
- Zhepei Wang
Citations in the corpus
- A General Framework for Uncertainty Estimation in Deep Learning (2019, methodology): “Following previous work [36], we use the RMSE metric be-tween the reference and actual trajectory for quantitative analysis.”
- Neural-Fly enables rapid learning for agile flight in strong winds (2022, background): “Another recent work focuses on deriving simplified rotor-drag models that are differentially flat (2).”
- Geometrically Constrained Trajectory Optimization for Multicopters (2021, methodology): “Consequently, it is applicable to incorporate more accurate dynamics and actuator limits for any differentially flat multicopters by considering more model details such as the rotor drag (Faessler et al. 2018).”
- Dynamic obstacle avoidance for quadrotors with event cameras (2020, background): “The output of this module is a velocity command, which is then fed to the position controller proposed in (54) and available as open source (http://rpg.”
- The current state and future outlook of rescue robotics (2019, background): “…would be to enable a flying robot to enter structures such as earthquake‐damaged buildings through small apertures in an emergency response (right; Falanga et al., 2018) [Color figure can be viewed at wileyonlinelibrary.com] F IGURE 6 Examples of flying robot applications within the disaster…”