arXiv：cs.LG· David Grasev·· 13 小时前
基于 Koopman 的双转子涡扇发动机非线性辨识与模型预测控制
Koopman-Based Nonlinear Identification and Model Predictive Control of a Turbofan Engine
AI 导读
研究用 Koopman 算子方法对双转子涡扇发动机进行多变量控制，提出一种元启发式扩展动态模式分解，可同时捕捉转速动态与发动机压比（EPR），构建单个可跨控制策略复用的 Koopman 模型。
正文
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 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 feedback linearization controller (K-FBLC) and its integrator-augmented version (K-FBLC-I). The Koopman representation further enables nonlinear GTE output limiters, such as rotor-acceleration and turbine-inlet-temperature limits, to be expressed as linear constraints in the AKMPC. The controllers are evaluated for two control 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 all strategies achieve comparable performance in sea-level conditions, the AKMPC demonstrates improved performance under varying flight conditions due to its ability to capture nonlinear dynamics, handle constraints, and compensate for model mismatch. Moreover, the EPR control strategy improves the thrust response. The study highlights the applicability of Koopman-based control and the advantages of the AKMPC framework for robust turbofan engine control.
| Comments: | 24 pages, 24 figures |
| Subjects: | Machine Learning (cs.LG); Systems and Control (eess.SY) |
| Cite as: | arXiv:2604.01730 [cs.LG] |
| (or arXiv:2604.01730v3 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.01730 arXiv-issued DOI via DataCite |
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| Journal reference: | IET Control Theory & Applications 20, no. 1 (2026): e70169 |
| Related DOI: | https://doi.org/10.1049/cth2.70169
DOI(s) linking to related resources |
Submission history
From: David Grasev [view email]
[v1]
Thu, 2 Apr 2026 07:51:59 UTC (3,550 KB)
[v2]
Wed, 5 Aug 2026 07:04:13 UTC (4,088 KB)
[v3]
Fri, 9 Oct 2026 11:55:31 UTC (4,088 KB)
来源：arXiv：cs.LG · arxiv.org