Koopman-Based Nonlinear Identification and Model Predictive Control of a Turbofan Engine
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 adapted, 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 that can be reused across multiple control strategies. Using the identified time-varying Koopman model, an adaptive Koopman-based model predictive controller (AKMPC) with a disturbance observer is developed and compared with a Koopman-based f...
| Authors | David Grasev |
|---|---|
| Primary category | cs.LG |
| Published | 2 April 2026 |
| Last updated at source | 9 October 2026 |
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