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Optimum Gain-Scheduling PID Controllers for Gas Turbine Engines Based on NARMAX and Neural Network Models

[+] Author Affiliations
Junxia Mu, David Rees

University of Glamorgan, Wales, UK

Neophytos Chiras

Praxis Critical Systems, Ltd., Bath, UK

Paper No. GT2003-38667, pp. 509-515; 7 pages
doi:10.1115/GT2003-38667
From:
  • ASME Turbo Expo 2003, collocated with the 2003 International Joint Power Generation Conference
  • Volume 1: Turbo Expo 2003
  • Atlanta, Georgia, USA, June 16–19, 2003
  • Conference Sponsors: International Gas Turbine Institute
  • ISBN: 0-7918-3684-3 | eISBN: 0-7918-3671-1
  • Copyright © 2003 by ASME

abstract

This paper presents PID controller designs based on NARMAX and feedforward neural network models of a Spey gas turbine engine. Both models represent the dynamic relationship between the fuel flow and shaft speed. Due to the engine non-linearity, a single set of PID controller parameters is not sufficient to control the gas turbine throughout the operating range. Gain-scheduling PID controllers are therefore used in order to obtain optimum control. A comparison between the controller designs based on the two model representations is also made.

Copyright © 2003 by ASME

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