OoD-Control: Out-of-Distribution Generalization for Adaptive UAV Flight Control

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 on 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.