A hybrid onboard model for aero-engine direct thrust predictive control
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Qiangang Zheng
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Liangliang Li
Abstract
Direct thrust control can markedly enhance thrust regulation accuracy and unlock the full performance potential of aero-engines. To improve both real-time capability and precision, we propose a hybrid adaptive onboard predictive modeling framework, termed DNN-PSM-SVM. In this approach, a deep neural network captures strong nonlinearities to refine accuracy, while steady- and dynamic-deviation models based on PSM and SVM reduce computational complexity. A Kalman filter further enhances adaptability, avoiding heavy nonlinear calculations and significantly improving real-time performance. Leveraging this model within a predictive control scheme, unmeasurable parameters such as thrust and surge margin are estimated in real-time, enabling accurate thrust control even under component degradation. Simulation results show that the method outperforms conventional predictive control, achieving steady-state accuracy below 0.06 % and improving real-time performance by nearly an order of magnitude. Unlike sensor-based control, it maintains precise thrust regulation despite engine degradation.
Acknowledgments
This study was supported in part by General Project of National Natural Science Foundation of China (No. 52372389), in part by National Science and Technology Major Project (J2022-I-0003-0003) in part by Aero Engine Corporation of China industry-university research cooperation project, China (No. HFZL2023CXY013).
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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Research funding: None declared.
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Data availability: Not applicable.
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