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

Asgari, H ; Sharif University of Technology | 2017

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  1. Type of Document: Article
  2. DOI: 10.1108/AEAT-11-2014-0187
  3. Publisher: Emerald Group Publishing Ltd , 2017
  4. Abstract:
  5. 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 artificial neural network-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 used. 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 PID controller. Practical implications - This study aims at using artificial intelligence in gas turbine control systems. Originality/value - This paper provides a novel methodology for control of gas turbines. © Emerald Publishing Limited
  6. Keywords:
  7. Control ; Gas turbine ; NARMA-L2 ; Neural network ; PID ; Feedback linearization ; Gas turbines ; Gases ; Low power electronics ; Neural networks ; Proportional control systems ; Three term control systems ; Control objectives ; Design/methodology/approach ; Gas turbine control ; Narma-L2 ; Network-based controllers ; Nonlinear auto-regressive moving averages ; Performance indices ; Single-shaft gas turbines ; Controllers
  8. Source: Aircraft Engineering and Aerospace Technology ; Volume 89, Issue 1 , 2017 , Pages 52-65 ; 00022667 (ISSN)
  9. URL: https://www.emeraldinsight.com/doi/abs/10.1108/AEAT-11-2014-0187