<ul><li>Gas turbine engines are complex nonlinear dynamical systems, making it challenging to derive physics-based models.</li><li>Conventional experimental methods for deriving component-level and locally linear parameter-varying models have limitations, addressed through data-driven identification techniques.</li><li>Rotor dynamics were estimated using sparse identification of nonlinear dynamics, followed by mapping into an optimally constructed Koopman eigenfunction space.</li><li>A globally optimal nonlinear feedback controller based on the Koopman model outperformed other benchmark controllers in reference tracking and disturbance rejection, showcasing superior performance under varying flight conditions.</li></ul>

Identification and Optimal Nonlinear Control of Turbojet Engine Using Koopman Eigenfunction Model

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