Model-based Acceleration Control of Turbofan Engines with a Hammerstein-Wiener Representation

Abstract

This version is available at https://strathprints.strath.ac.uk/55823/ Strathprints is designed to allow users to access the research output of the University of Strathclyde. Unless otherwise explicitly stated on the manuscript, Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Please check the manuscript for details of any other licences that may have been applied. You may not engage in further distribution of the material for any profitmaking activities or any commercial gain. You may freely distribute both the url (https://strathprints.strath.ac.uk/) and the content of this paper for research or private study, educational, or not-for-profit purposes without prior permission or charge.Any correspondence concerning this service should be sent to the Abstract: Acceleration control of turbofan engines is conventionally designed through either schedule-based or acceleration-based approach. With the widespread acceptance of model-based design in aviation industry, it becomes necessary to investigate the issues associated with model-based design for acceleration control. In this paper, the challenges for implementing model-based acceleration control are explained; a novel Hammerstein-Wiener representation of engine models is introduced; based on the Hammerstein-Wiener model, a nonlinear generalized minimum variance type of optimal control law is derived; the feature of the proposed approach is that it does not require the inversion operation that usually upsets those nonlinear control techniques. The effectiveness of the proposed control design method is validated through a detailed numerical study.

Linear Transfer Functions Extraction for a Power Generation Three-Shaft Gas Turbine Based on Actual Specifications

“…The methods utilized for nonlinear system modeling are the Hammerstein, Wiener, and Hammerstein–Wiener methods. 30–38 The nonlinear models in question are of the block-structure models, consisting of a linear dynamic section and a static nonlinear section. The general structure of these methods has been shown in Figure 3.…”

“…19,20 The methods based on model identification have been utilized as well-known methods for the purpose of generating dynamic models of the gas turbine, and are classified as linear and nonlinear methods. Many of the studies conducted the field have been based on nonlinear methods and noticeable papers have been published in the field of identification based on NARX and NARMAX, [21][22][23][24][25] artificial neural networks, 26,27 fuzzy, 28,29 liner-nonlinear block structure methods (Wiener), [30][31][32] Hammerstein, 33,34 Wiener-Hammerstein, [35][36][37][38] etc.…”

Aeroengine High-Attitude/Low Mach Number Oscillations: Mechanism and Prevention Design

“…In this respect, neural networks [16], generic algorithms [17], NARMAX [18], generalized describe function [19], and Hammerstein-Wiener representation [20] methods are all utilized for nonlinear model identifications of aeroengines. The corresponding nonlinear design approaches are then deployed for aeroengine control, see [21,22] and references therein.…”