Development and Implementation of Physics-Informed Neural ODE for Dynamics Modeling of a Fixed-Wing Aircraft Under Icing/Fault

Abstract

Accurate dynamics modeling is crucial for the safety and control of fixed-wing aircraft under perturbation (e.g. icing/fault). In this work, we propose a physics-informed Neural Ordinary Differential Equation (PI-NODE)-based scheme for aircraft dynamics modeling under icing/fault. First, icing accumulation and control surface faults are considered and injected into the nominal (clean) aircraft dynamics model. Second, the physics knowledge of aircraft dynamics modeling is divided into kinematics and kinetics. The former is universally applicable and borrows directly from the nominal aircraft. The latter kinetics knowledge, which hinges on external forces and moments, is inaccurate and challenging under icing/fault. To address this issue, we employ Neural ODE to compensate for the residual between the aircraft dynamics under icing/fault and the nominal (clean) condition, resulting in a naturally continuous-time modeling approach. In experiments, we benchmark the proposed PI-NODE against three baseline methods in a dedicated flight scenario. Comparative studies validate the higher accuracy and improve the generalization ability of the proposed PI-NODE for aircraft dynamics modeling under icing/fault.