Multi‐Objective Optimisation of Flight Simulation Device Data Package Based on Neural Network Surrogate Models

ABSTRACT To demonstrate compliance of flight simulation device (FSD) data packages with regulatory requirements, certification subjects must meet multiple error tolerance objectives. This paper presents a rapid parameter tuning method based on multi‐objective optimisation utilising neural network surrogate models, which improves the efficiency of multi‐system joint simulation. In comparison to the simulation data before optimisation, the average mean relative error for the longitudinal characteristics of the 2c1a subject was reduced by 70.2%, while still adhering to various optimisation constraints. The research outlined in this paper indicates a significant enhancement in the efficiency of parameter tuning and model integration verification, facilitating the fulfillment of certification requirements for advanced training FSD.