OOD-CONTROL: OUT-OF-DISTRIBUTION GENERAL-IZATION FOR ADAPTIVE UAV FLIGHT CONTROL

Abstract

Data-driven control methods have demonstrated precise and agile control of Unmanned Aerial Vehicles (UAVs) over turbulence environments. However, they are relatively weak at taming the out-of-distribution (OoD) data, i.e., encountering the generalization problem when faced with unknown environments with different data distributions from the training set. Many studies have designed algorithms to reduce the impact of the OoD problem, a common but tricky problem in machine learning. To tackle the OoD generalization problem in control, we propose a theoretically guaranteed approach: OoD-Control. We provide proof that for any perturbation within some range on the states, the control error can be upper bounded by a constant. In this paper, we present our OoD-Control generalization algorithm for online adaptive flight control and execute it in two instances. Experiments show that systems trained by the proposed OoD-Control algorithm perform better in quite different environments from training. And the control method is extensible and pervasively applicable and can be applied to different dynamical models. OoD-Control is validated on UAV dynamic models, and we find it performs state-of-the-art in positioning stability and trajectory tracking problems.

1. INTRODUCTION

UAVs have gained considerable attention and are widely used for various purposes because of their high manoeuvrability and flexibility. For example, quadrotors are widely deployed for inspection, reconnaissance, and rescue. As control strategies evolve, novel scenarios for UAVs, such as aerial grasping, transporting, and bridge inspection (Ruggiero et al., 2018) , require more precise trajectory tracking. Especially in the outdoor environment, unpredictable and changing wind field conditions pose substantial challenges to the stability of UAVs. Rotor blades are affected by induced airflow caused by the wind, which creates complex and non-stationary aerodynamic interactions (see Appendix B.6.3). From security and policy perspectives, demonstrating that UAVs can operate safely and reliably in unpredictable environments with various distributions is an essential requirement. It is also the premise for future medical robots, autonomous cars, and manned aerial vehicles to be widely accepted. Many areas have benefited from data-driven approaches. However, they are susceptible to performance degradation after generalization. And the majority of deep learning algorithms heavily rely on the I.I.D assumption for data, which is generally violated in practice due to domain generalization (Zhou et al., 2022) . Nevertheless, neural networks may lose their robustness when confronted with OoD data. Many cases of failure in DNN originate from shortcut learning in the learning process (Geirhos et al., 2020) . The damage to the UAV is undoubtedly considerable if the UAV cannot adjust to the changing environment, i.e., it is unstable or even crashes in an OoD situation. One significant objective of this paper is to propose a control algorithm to enable UAVs to maintain accurate control even in the case of environment domain shifts. Our Contributions. UAVs interact with the changing environment, resulting in complex environment-dependent uncertain aerodynamics, called unknown dynamics, that are tricky to model and significantly impact precise control. Previous data-driven controllers attempt to solve the problem by estimating the unknown dynamics, while the estimation accuracy and the performance of the controllers are limited by the environment domain shifts in tests. This paper presents a methodology

