Multi-Parametric Model Predictive Control Strategy on Laboratory SR-30 Gas Turbine

Abstract

This paper deals with the development of multi-parametric Model Predictive Control (mp-MPC) strategies for a laboratory SR-30 gas turbine setup. The objective is to control the engine speed and turbine exhaust pressure of the gas turbine. Firstly, an empirical transfer function model is obtained experimentally, between the fuel flow-shaft speed and nozzle diameter-turbine exhaust pressure (TEP) of the gas turbine. Then, Model Predictive Control (MPC) is designed based on the empirical models. The output responses under MPC are found to be satisfactory, however, with large computational time. Next, mp-MPC controllers are designed based on the empirical models. Relevant operating constraints are also added as design specifications. It is found that the multi-parametric MPC delivers superior results in comparison with conventional MPC, in terms of computational time, while delivering the same transient performance.