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