[2604.01730] Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine

Computer Science > Machine Learning arXiv:2604.01730 (cs) [Submitted on 2 Apr 2026] Title:Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine Authors:David Grasev View a PDF of the paper titled Koopman-Based Nonlinear Identification and Adaptive Control of a Turbofan Engine, by David Grasev View PDF HTML (experimental) Abstract:This paper investigates Koopman operator-based approaches for multivariable control of a two-spool turbofan engine. A physics-based component-level model is developed to generate training data and validate the controllers. A meta-heuristic extended dynamic mode decomposition is developed, with a cost function designed to accurately capture both spool-speed dynamics and the engine pressure ratio (EPR), enabling the construction of a single Koopman model suitable for multiple control objectives. Using the identified time-varying Koopman model, two controllers are developed: an adaptive Koopman-based model predictive controller (AKMPC) with a disturbance observer and a Koopman-based feedback linearization controller (K-FBLC), which serves as a benchmark. The controllers are evaluated for two control strategies, namely configurations of spool speeds and EPR, under both sea-level and varying flight conditions. The results demonstrate that the proposed identification approach enables accurate predictions of both spool speeds and EPR, allowing the Koopman model to be reused flexibly across different control formulations. While ...