Startseite An active fault-tolerant control strategy of aircraft engines based on multi-model predictive control
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An active fault-tolerant control strategy of aircraft engines based on multi-model predictive control

  • Xian Du und Yan-Hua Ma EMAIL logo
Veröffentlicht/Copyright: 16. September 2020
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Abstract

In order to mitigate or even eliminate the adverse effects caused by typical components faults of aircraft engines, an active fault-tolerant strategy based on multi-model predictive control is proposed, which consists of a pre-established multi-model library, a judgement module, and corresponding predictive controllers with smooth transition switching logic. Multiple dynamic nonlinear or linear models are firstly established by means of system identification methods, based on the component-level nonlinear engine model or historical data in faults cases. The judgement module is utilized to online compare the engine measured outputs with that of all models in the pattern library and select the best matched dynamic model on the basis of outputs error quadratic performance index, thus determining the most appropriate predictive controller for the next control sample period. When a certain fault occurs, the fault model in the library could be identified and fault-model based predictive controller is activated. Finally, two kinds of pre-considered high-pressure compressor and high-pressure turbine component-level faults are taken as an example to design the active fault-tolerant controller. Simulation results show that the judgement module owns the ability to sense the fault and gives smooth switching signal to the suitable predictive controller, verifying the effectiveness of the proposed technique.


Corresponding author: Yan-Hua Ma, School of Microelectronics, Dalian University of Technology, Dalian, China; DUT Artificial Intelligence Institute, Dalian, China, E-mail:

Award Identifier / Grant number: 61903059

Award Identifier / Grant number: 61903061

Award Identifier / Grant number: 61890924

Funding source: LiaoNing Revitalization Talents Program

Award Identifier / Grant number: XLYC1907070

Funding source: Aviation Science Foundation of China

Award Identifier / Grant number: 2019ZB063001

Funding source: National Science and Technology Major Project

Award Identifier / Grant number: 2017-V-0011-0062

  1. Author contribution: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: This work is partly supported by the National Natural Science Foundation of China (Grant No. 61903059, 61903061, 61890924), and partly supported by LiaoNing Revitalization Talents Program (Grant No. XLYC1907070), and partly supported by Aviation Science Foundation of China (Grant No. 2019ZB063001) and National Science and Technology Major Project (Grant No. 2017-V-0011-0062).

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

  4. Copyright reminder: If any material that is included in a paper is under copyright, the authors of the paper are responsible for obtaining copyright permissions, including any information required by the copyright holder, and be able to produce such permissions upon request.

References

1. Jaw, LC, Mattingly, JD. Aircraft engine controls: design, system analysis, and health monitoring. Reston: Alexander, USA: AIAA Education Series; 2009. pp. 40–65.10.2514/4.867057Suche in Google Scholar

2. Garg, S. Aircraft engine controls and diagnostics research in support of NASA aeronautics research mission programs. In: 46th AIAA/ASME/SAE/ASEE joint propulsion conference & exhibit. Nashville, Tennessee, USA; July 2010. pp. 25–8.10.2514/6.2010-6747Suche in Google Scholar

3. Volponi, AJ. Gas turbine engine health management: past,present, and future trends. J Eng Gas Turbines Power 2014;136: 051201. https://doi.org/10.1115/1.4026126.Suche in Google Scholar

4. Kumar, A, Viassolo, DE. Model-based fault tolerant control. NASA report. NASA/CR-2008-215273. Cleveland, Ohio: National Aeronautics and Space Administration.Suche in Google Scholar

5. Saluru, D, Yedavalli, R, Belapurkar, R. Active fault tolerant model predictive control of a turbofan engine using C-MAPSS40k.In: 5th Annual ASME Dynamic Systems and Control Conference (DSCC), Fort Lauderdale 2012.10.1115/DSCC2012-MOVIC2012-8730Suche in Google Scholar

6. Baldini, A, Fasano, A, Felicetti, R. A model-based active fault tolerant control scheme for a remotely operated vehicle. IFAC 2018;51:798–805. https://doi.org/10.1016/j.ifacol.2018.09.666.Suche in Google Scholar

7. Mirzaee, A, Salahshoor, K. Fault diagnosis and accommodation of nonlinear systems based on multiple-model adaptive unscented Kalman filter and switched MPC and H-infinity loop-shaping controller. J Process Contr 2012;22:626–34. https://doi.org/10.1016/j.jprocont.2012.01.002.Suche in Google Scholar

8. Yuan, Y, Ding, S, Liu, X. Hybrid diagnosis system for aeroengine sensor and actuator faults. J Aero Eng 2020;33:04019108.10.1061/(ASCE)AS.1943-5525.0001105Suche in Google Scholar

9. Xiao, L, Du, Y, Hu, J. Sliding mode fault tolerant control with adaptive diagnosis for aircraft engines. Int J Turbo Jet Engines 2018;35:49–57. https://doi.org/10.1515/tjj-2016-0023.Suche in Google Scholar

10. Zarch, M, Puig, V, Poshtan, J, Shoorehdeli, MA. Actuator fault tolerance evaluation approach of nonlinear model predictive control systems using viability theory. J Process Contr 2018;71:35–45. https://doi.org/10.1016/j.jprocont.2018.08.006.Suche in Google Scholar

11. Lu, F, Gao, TY, Huang, JQ, Qiu, XJ. Nonlinear Kalman filters for aircraft engine gas path health estimation with measurement uncertainty. Aero Sci Technol 2018;76:126–40. https://doi.org/10.1016/j.ast.2018.01.024.Suche in Google Scholar

12. Volponi, AJ, Depold, H, Ganguli, R. The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study. J Eng Gas Turbines Power 2003;125:917–24. https://doi.org/10.1115/1.1419016.Suche in Google Scholar

13. Kobayashi, T, Simon, DL. Hybrid Kalman filter: a new approach for aircraft engine in-flight diagnostics. NASA report. NASA/TM-2006-214491. Cleveland, Ohio: National Aeronautics and Space Administration.10.1115/GT2006-90870Suche in Google Scholar

14. Huang, KM, Yin, ZY, Yang, ZS. Study of rotor speed sensors fault accommodation and test run of turbo-shaft engine. J Aero Power 2007;22:280–4.Suche in Google Scholar

15. Yang, ZS, Qiu, XJ, Zhuang, XM, Huang, JQ. Aero-engine fault-tolerant control based on mode switch. J Aero Power 2014;29:953–64.Suche in Google Scholar

16. Rausch, RT, Goebel, KF, Eklund, NH. Integrated in-flight fault detection and accommodation: a model-based study. J Eng Gas Turbines Power 2007;129:962–9. https://doi.org/10.1115/1.2720517.Suche in Google Scholar

17. Camacho, EF, Alamo, T, la Pena, D. Fault-tolerant model predictive control. In: IEEE Conference on Emerging Technologies and Factory Automation (ETFA), Bilbao, Spain; 13–16 September 2010.10.1109/ETFA.2010.5641226Suche in Google Scholar

18. Kale, MM, Chipperfield, AJ. Stabilized MPC formulations for robust reconfigurable flight control. Contr Eng Pract 2005;13:771–88. https://doi.org/10.1016/j.conengprac.2004.09.001.Suche in Google Scholar

19. De Almeida, FA. Trajectory tracking with fault-tolerant flight control system: a model predictive control approach. Germany: Shaker; 2009. pp. 45–55.Suche in Google Scholar

20. Maciejowski, J, Jones, C. MPC fault-tolerant flight control case study: flight 1862. In: Proceedings of the international federation of automatic control on safeprocess sympoisum, Washington, USA 2003, EPFL-CONF-169763. pp. 119–24.10.1016/S1474-6670(17)36480-7Suche in Google Scholar

21. Richter, H. Advanced control of turbofan engines. New York, USA: Springer; 2011. pp. 72–98.10.1007/978-1-4614-1171-0Suche in Google Scholar

22. Richter, H, Singaraju, A, Litt, JS. Multiplexed predictive control of a large commercial turbofan engine. J Guid Contr Dynam 2008;31:273–81. https://doi.org/10.2514/1.30591.Suche in Google Scholar

23. Du, X, Sun, XM, Wang, ZM, Dai, AN. A scheduling scheme of linear model predictive controllers for turbofan engines. IEEE Access 2017;5:24533–41. https://doi.org/10.1109/access.2017.2764076.Suche in Google Scholar

24. Rawlings, JB, Mayne, DQ. Model predictive control: theory and design. Santa Barbara, California: Nob Hill Publishing; 2009. pp. 28–49.Suche in Google Scholar

25. Liu, X, An, S. Smooth switching controller design for multi-objective control systems and applications. J Aero Eng 2016;29:1–13.10.1061/(ASCE)AS.1943-5525.0000596Suche in Google Scholar

Received: 2020-08-11
Accepted: 2020-08-14
Published Online: 2020-09-16
Published in Print: 2023-03-28

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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