Control-Oriented System Identification of Turbojet Dynamics

Abstract

The autonomous operation of turbojets requires reliable, accurate, and manageable dynamical models for several key processes. This article describes a practical robust method for obtaining turbojet thrust and shaft speed models from experimental data. The proposed methodology combines several data mining tools with the intention of handling typical difficulties present during experimental turbojet modeling, such as high noise levels and uncertainty in the plant dynamics. The resulting shaft speed and thrust models achieved a percentage error of 0.8561% and 3.3081%, respectively, for the whole operating range. The predictive power of the resulting models is also assessed in the frequency domain. The turbojet cut frequencies are experimentally determined and were found to match those predicted by the identified models. Finally, the proposed strategy is systematically tested with respect to popular aeroengine models, outperforming them both in the time and frequency domains. These results allow us to conclude that the proposed modeling method improves current modeling approaches in both manageability and predictive power.