A physics-based neural network for flight dynamics modelling and simulation

Abstract

The authors have developed a novel physics-based nonlinear autoregressive exogeneous neural network model architecture for flight modelling across the entire flight envelope, called FlyNet. When using traditional parameter estimation and output-error methods, aircraft models are captured about a single point in the flight envelope using a first-order Taylor series to approximate forces and moments. To enable analysis throughout the entire operational envelope, the traditional models can be extended by interpolating or stitching between a number of these single-condition models. This paper completes the evolutionary next step in aircraft modelling to consider all second-order Taylor series terms instead of a subset of those and by exploiting the ability of neural networks to capture more complex and nonlinear behaviour for the efficient development of a continuous flight simulation model valid across the entire envelope. This method is valid for fixed- and rotary-wing aircraft. The behaviour of a conventional model is compared to FlyNet using flight test data collected from the National Research Council of Canada’s Bell 412HP in forward flight.

Hamiltonian Neural Network 6-DoF Rigid-Body Dynamic Modeling Based on Energy Variation Estimation

“…Tis method is employed to solve partial diferential equations with physical constraints or to incorporate partial diferential equations directly into the neural network learning process. For example, PINNs have been utilized to replicate 6-DoF equations of aircraft dynamics [1]. However, due to potential interference or errors in rigid-body fight data, the rigid-body dynamic model generated by the PINN method may be afected by interference, compromising its accuracy.…”