An enhanced non-iterative real-time solver via multilayer perceptron for on-board component-level models

Abstract

The real-time on-board component-level model (CLM) of gas turbine engines (GTEs) is an important foundation for fault-tolerant control and health management. However, traditional iterative solving algorithms typically rely on the multiple computations of gas-path thermodynamic parameters, which seriously limits their practical application. In this paper, aiming to improve the real-time performance of CLM, an enhanced non-iterative real-time solver (ENRS) method is proposed. It has combined an input selection strategy, an under-sampling supplemental enhancement training (USET) technique, and a multilayer perceptron (MLP) network. Compared with the existing CLM solving methods, its innovations are as follows: (1) a novel framework for directly solving CLM is proposed, which helps get rid of the dilemma of increased time consumption due to multiple iterations. (2) an enhanced training process is designed for the MLP network, which helps improve the adaptivity to the full flight envelope and all engine states, especially to reduce the maximum error. Simulation tests eventually show that the ENRS effectively improves the real-time performance of CLM in practical applications, and maintains solution accuracy and robustness.