Design of Conventional and Neural Network Based Controllers for a Single-Shaft Gas Turbine

Type of content
Journal Article
Publisher's DOI/URI
Thesis discipline
Degree name
Publisher
University of Canterbury. Mathematics and Statistics
University of Canterbury. Mechanical Engineering
Journal Title
Journal ISSN
Volume Title
Language
Date
2016
Authors
Asgari, H.
Chen, X.Q.
Jegarkandi, M.F.
Sainudiin, R.
Abstract

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.

Description
Citation
Asgari, H., Chen, X.Q., Jegarkandi, M.F., Sainudiin, R. (2016) Design of Conventional and Neural Network Based Controllers for a Single-Shaft Gas Turbine. Aircraft Engineering and Aerospace Technology, (in press).
Keywords
gas turbine, neural network, control, PID, NARMA-L2
Ngā upoko tukutuku/Māori subject headings
ANZSRC fields of research
Field of Research::09 - Engineering::0906 - Electrical and Electronic Engineering::090602 - Control Systems, Robotics and Automation
Field of Research::09 - Engineering::0913 - Mechanical Engineering::091302 - Automation and Control Engineering
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