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.
Optimum Gain-Scheduling PID Controllers for Gas Turbine Engines Based on NARMAX and Neural Network Models
Mu, J, Rees, D, & Chiras, N. "Optimum Gain-Scheduling PID Controllers for Gas Turbine Engines Based on NARMAX and Neural Network Models." Proceedings of the 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. pp. 509-515. ASME. https://doi.org/10.1115/GT2003-38667
Download citation file: