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

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 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.