Koopman Eigenfunction-Based Identification and Optimal Nonlinear Control of Turbojet Engine

TL;DR

This paper introduces a novel data-driven approach using Koopman eigenfunctions for identifying and designing optimal nonlinear controllers for complex gas turbine engines, improving performance over traditional methods.

Contribution

It develops a Koopman eigenfunction-based framework for system identification and control of nonlinear engines, leveraging eigenvalue optimization and validation against reference models.

Findings

Koopman model accurately captures engine dynamics.

Koopman-based controller outperforms traditional controllers.

Enhanced disturbance rejection and tracking performance.

Abstract

Gas turbine engines are complex and highly nonlinear dynamical systems. Deriving their physics-based models can be challenging because it requires performance characteristics that are not always available, often leading to many simplifying assumptions. This paper discusses the limitations of conventional experimental methods used to derive component-level and locally linear parameter-varying models, and addresses these issues by employing identification techniques based on data collected from standard engine operation under closed-loop control. The rotor dynamics are estimated using the sparse identification of nonlinear dynamics. Subsequently, the autonomous part of the dynamics is mapped into an optimally constructed Koopman eigenfunction space. This process involves eigenvalue optimization using metaheuristic algorithms and temporal projection, followed by gradient-based eigenfunction identification. The resulting Koopman model is validated against an in-house reference component-level model. A globally optimal nonlinear feedback controller and a Kalman estimator are then designed within the eigenfunction space and compared to traditional and gain-scheduled proportional-integral controllers, as well as a proposed internal model control approach. The eigenmode structure enables targeting individual modes during optimization, leading to improved performance tuning. Results demonstrate that the Koopman-based controller surpasses other benchmark controllers in both reference tracking and disturbance rejection under sea-level and varying flight conditions, due to its global nature.