Jet Engine Model for Control and Real-Time Simulations

Lichtsinder, Michael; Levy, Y.

doi:10.1115/1.1915391

Cited by 60 publications

(40 citation statements)

References 7 publications

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“…In fact, it is well-known that the aerothermal component-level models can capture the engine dynamics very accurately [7][8][9][10][11][12]. However the component-level models are not control-oriented due to the fact the key components such as fan, compressor, and turbine are represented by lookup tables or some other non-analytical modules such as (C or Fortran) program codes, while (unfortunately) most of the control theories assume that an analytical model exists before control design.…”

Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2017

International Journal of Turbo &Amp; Jet-Engines

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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.

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Section: Introductionmentioning

confidence: 99%

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

Wang

et al. 2017

International Journal of Turbo &Amp; Jet-Engines

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“…They stated that their methodology could provide more flexibility in design of strategies, controlling the speed and surge simultaneously, and accelerating the compressor without stalling problems. A simple and fast linear model for real-time transient performance of a jet engine control was developed by Lichtsinder and levy (2006) . Pongraz et al (2008) used an input-output linearization method to design an adaptive reference tracking controller for a low-power gas turbine model.…”

Section: White-box Modelsmentioning

confidence: 99%

Design of conventional and neural network based controllers for a single-shaft gas turbine

Asgari

Jegarkandi

Chen

et al. 2017

AEAT

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Purpose -The purpose of this paper is to develop and compare conventional and neural network based controllers for gas turbines.Design/methodology/approach -Design of two different controllers is considered. These controllers consist of a NARMA-L2 which is an ANN-based nonlinear autoregressive moving average (NARMA) controller with feedback linearization, and a conventional proportional-integrator-derivative (PID) controller for a low-power aero gas turbine.They are briefly described and their parameters are adjusted and tuned in Simulink-MATLAB environment according to the requirement of the gas turbine system and the control objectives. For this purpose, Simulink and neural network based modelling is employed. Performances of the controllers are explored and compared on the base of design criteria and performance indices.Findings -It is shown that NARMA-L2, as a neural network based controller, has a superior performance to the PID controller.Practical implications -Using artificial intelligence in gas turbine control systems.Originality/value -Providing a novel methodology for control of gas turbines.

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“…Simulink provides an easy-to-use, graphical, modeling and simulation development environment for developing time-based simulations in a wide range of applications, and Simulink is capable of code generation using associated tools [17][18][19]. So, a dynamic model for a specific turbojet engine is developed using the MATLAB simulation environment and its Simulink toolbox [1].…”

Section: Turbine Engine Simulator Model (Tesm)mentioning

confidence: 99%

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

Bazazzadeh

Badihi

Shahriari

2011

International Journal of Aerospace Engineering

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This paper presents a successful approach in designing a Fuzzy Logic Controller (FLC) for a specific Jet Engine. At first, a suitable mathematical model for the jet engine is presented by the aid of SIMULINK. Then by applying different reasonable fuel flow functions via the engine model, some important engine-transient operation parameters (such as thrust, compressor surge margin, turbine inlet temperature, etc.) are obtained. These parameters provide a precious database, which train a neural network. At the second step, by designing and training a feedforward multilayer perceptron neural network according to this available database; a number of different reasonable fuel flow functions for various engine acceleration operations are determined. These functions are used to define the desired fuzzy fuel functions. Indeed, the neural networks are used as an effective method to define the optimum fuzzy fuel functions. At the next step, we propose a FLC by using the engine simulation model and the neural network results. The proposed control scheme is proved by computer simulation using the designed engine model. The simulation results of engine model with FLC illustrate that the proposed controller achieves the desired performance and stability.

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Jet Engine Model for Control and Real-Time Simulations

Cited by 60 publications

References 7 publications

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

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

Design of conventional and neural network based controllers for a single-shaft gas turbine

Gas Turbine Engine Control Design Using Fuzzy Logic and Neural Networks

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