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Research on the control method of turbine outlet temperature limitation protection based on nonlinear state estimation

  • Yuan Liu , Qiuying Yan , Bingxiong Yin , Rui Tan , Hui Fang , Jindong Wu and Wei Li EMAIL logo
Published/Copyright: January 9, 2026
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Abstract

Developing multivariable control schedules for turboshaft engines addresses critical safety demands in aero-engine performance. This study introduces a turbine outlet temperature (TOT) limitation protection method using nonlinear state estimation. A turboshaft engine multivariable controller is designed, complemented by a Bidirectional Long Short-Term Memory (BLSTM) network-based onboard model. Validation shows less than 2 % relative error between the BLSTM model and the component-level engine model. To enhance precision, an Unscented Kalman State Estimator (UKSE) leverages residuals from power turbine speed data to estimate TOT (parameter T45), achieving a 1.4 % estimation error. The UKSE framework enables proactive temperature limitation control by dynamically adjusting protection thresholds. Experimental tests under two flight conditions confirm the method’s efficacy: 1) TOT exceedance magnitude decreases by 75 %, 2) overtemperature duration shortens by over 50 %, and 3) temperature violations are fully eliminated in specific scenarios. These results highlight the integration of BLSTM modeling and UKSE-based estimation as a robust strategy for mitigating turbine thermal risks while maintaining operational stability. The approach bridges model-based control and real-time adaptive protection, offering a scalable solution for next-generation engine safety systems.


Corresponding author: Wei Li, AECC Hunan Aviation Powerplant Research Institute, Zhuzhou, 412000, China, E-mail:

  1. Research ethics: Not applicable.

  2. Informed consent: Informed consent was obtained from all individuals included in this study, or their legal guardians or wards.

  3. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  4. Use of Large Language Models, AI and Machine Learning Tools: None declared.

  5. Conflict of interest: All other authors state no conflict of interest.

  6. Research funding: None declared.

  7. Data availability: Not applicable.

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Received: 2025-05-25
Accepted: 2025-08-13
Published Online: 2026-01-09

© 2025 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 19.1.2026 from https://www.degruyterbrill.com/document/doi/10.1515/tjj-2025-0059/pdf
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