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This paper introduces Adaptive SINDy, a novel control algorithm for UAVs that integrates Sparse Identification of Non-Linear Dynamics (SINDy) with Recursive Least Squares (RLS) adaptive control to reject wind disturbances. The approach learns a data-driven model of residual forces using SINDy and adapts to turbulent environments through RLS. Experiments on a Crazyflie drone in a dynamic environment with winds up to 2 m/s demonstrate that Adaptive SINDy outperforms PID and INDI controllers in trajectory tracking, achieving RMSEs of 12.2 cm and 17.6 cm on circular and lemniscate trajectories, respectively.
Adaptive SINDy lets drones fly smoother in turbulence by learning to predict and counteract wind gusts better than traditional controllers.
The stability and control of Unmanned Aerial Vehicles (UAVs) in a turbulent environment is a matter of great concern. Devising a robust control algorithm to reject disturbances is challenging due to the highly nonlinear nature of wind dynamics, and modeling the dynamics using analytical techniques is not straightforward. While traditional techniques using disturbance observers and classical adaptive control have shown some progress, they are mostly limited to relatively non-complex environments. On the other hand, learning based approaches are increasingly being used for modeling of residual forces and disturbance rejection; however, their generalization and interpretability is a factor of concern. To this end, we propose a novel integration of data-driven system identification using Sparse Identification of Non-Linear Dynamics (SINDy) with a Recursive Least Square (RLS) adaptive control to adapt and reject wind disturbances in a turbulent environment. We tested and validated our approach on Gazebo harmonic environment and on real flights with wind speeds of up to 2 m/s from four directions, creating a highly dynamic and turbulent environment. Adaptive SINDy outperformed the baseline PID and INDI controllers on several trajectory tracking error metrics without crashing. A root mean square error (RMSE) of up to 12.2 cm and 17.6 cm, and a mean absolute error (MAE) of 13.7 cm and 10.5 cm were achieved on circular and lemniscate trajectories, respectively. The validation was performed on a very lightweight Crazyflie drone under a highly dynamic environment for complex trajectory tracking.
