Adaptive SINDy: Residual Force System Identification Based UAV Disturbance Rejection

TL;DR

This paper introduces an adaptive SINDy-based system identification method combined with RLS control to improve UAV disturbance rejection in turbulent environments, demonstrating superior performance over traditional controllers in real and simulated tests.

Contribution

The paper presents a novel integration of SINDy with RLS adaptive control for UAV disturbance rejection, enhancing robustness and interpretability in turbulent conditions.

Findings

Outperformed PID and INDI controllers in trajectory tracking accuracy.

Achieved RMSE up to 12.2 cm and 17.6 cm on different trajectories.

Validated on real Crazyflie drone in highly dynamic turbulence.

Abstract

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.