Skip to main content
SpringerLink
Log in

A Study on Application of Fuzzy Adaptive Unscented Kalman Filter to Nonlinear Turbojet Engine Control

  • Original Paper
  • Published:
International Journal of Aeronautical and Space Sciences Aims and scope Submit manuscript

Abstract

Safe and efficient flight powered by an aircraft turbojet engine relies on the performance of the engine controller preventing compressor surge with robustness from noises or disturbances. This paper proposes the effective nonlinear controller associated with the nonlinear filter for the real turbojet engine with highly nonlinear dynamics. For the feasible controller study the nonlinearity of the engine dynamics was investigated by comparing the step responses from the linearized model with the original nonlinear dynamics. The fuzzy-based PID control logic is introduced to control the engine efficiently and FAUKF is applied for robustness from noises. The simulation results prove the effectiveness of FAUKF applied to the proposed controller such that the control performances are superior over the conventional controller and the filer performance using FAUKF indicates the satisfactory results such as clearing the defects by reducing the distortions without compressor surge, whereas the conventional UKF is not fully effective as occurring some distortions with compressor surge due to a process noise.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Similar content being viewed by others

References

  1. Austin Spang III H, Brown H (1999) Control of jet engines. Control Eng Pract 7:1043–1059

    Article  Google Scholar 

  2. Sellers JF, Daniele CJ (1975) DYGEN—a program for calculating steady-state and transient performance of turbojet and turbofan engines. NASA TN D-7901

  3. Zilouchian A, Juliano M, Healy T, Davis J (2000) Design of a fuzzy logic controller for a jet engine fuel system. Control Eng Pract 8(8):873–883

    Article  Google Scholar 

  4. Chipperfield AJ, Bica B, Fleming PJ (2002) Fuzzy scheduling control of a gas turbine aero-engine: a multiobjective approach. IEEE Trans Ind Electron 49(3):536–548

    Article  Google Scholar 

  5. Han DJ (2002) A study on the design of fuzzy controller for a turbojet engine model and its performance enhancement through satisfactory multiple objectives. J Korean Soc Aeronaut Space Sci 31(6):61–71

    Google Scholar 

  6. Volponi A, DePold H, Ganguli R, Daguang C (2003) The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study. J Eng Gas Turbines Power 125:917–924

    Article  Google Scholar 

  7. Kobayashi T, Simon DL (2006) Hybrid Kalman filter approach for aircraft engine in-flight diagnostics: sensor fault detection case. NASA/TM-2006-214418

  8. Garg S (2007) Engine performance deterioration mitigation control—a retrofit approach. Aerospace guidance and control system committee meeting, Boulder, CO. http://www.csdy.umn.edu/acgsc/Meeting99//SubcommitteeE/SAE%20gGarg.PDF

  9. Whalley R, El Hassan T (2012) Turbo-jet engine multivariable control. In: Proceedings of the world congress on engineering and computer science, vol 2

  10. Amirante R, Catalano LA, Tamburrano P (2012) Thrust control of small turbojet engines using fuzzy logic: design and experimental validation. J Eng Gas Turbines Power 134(12):121601–121601-7

  11. Takahisa K, Donald LS (2003) Application of a bank of Kalman filters for aircraft engine fault diagnostics. NASA/TM-2003-212526

  12. Donald LS, Jeffrey BA (2012) An integrated approach for aircraft engine performance estimation and fault diagnostics. NASA/TM-2012-217725

  13. Brunell BJ, Bitmead R, Connolly AJ (2002) Nonlinear model predictive control of an aircraft gas turbine engine. In: Proceedings of the 41st IEEE conference on decision and control, pp 4649–4651

  14. Mirzaee A, Salahshoor K (2013) Fault tolerant control of an industrial gas turbine based on a hybrid fuzzy adaptive unscented Kalman filter. J Eng Gas Turbines Power 135(12):122501–122501-13

    Article  Google Scholar 

  15. Lu F, Huang J, Yiqiu L (2013) Gas path health monitoring for a turbofan engine based on a nonlinear filtering approach. Energies 6:492–513

    Article  Google Scholar 

  16. Julier SJ (2002) The scaled unscented transformation. Proc Am Control Conf 6(6):4555–4559

    Google Scholar 

  17. Julier SJ, Uhlmann JK (2004) Unscented filtering and nonlinear estimation. Proc IEEE 92(3):401–422

    Article  Google Scholar 

  18. Rhudy ME, Gu Y (2013) Understanding nonlinear Kalman filters, part II: an implementation guide. Interactive Robotics Letters, Tutorial

  19. Gustafsson F (2010) Particle filter theory and practice with positioning applications. IEEE A&E Syst Mag 25(7):53–81

    Article  Google Scholar 

  20. Jazwinski A (2007) Stochastic process and filtering theory. Academic Press, New York

    MATH  Google Scholar 

  21. Chen Z Bayesian filtering: from Kalman filters to particle filters, and beyond. MANUSCRIPT. http://www.dsi.unifi.it/users/chisci/idfric/Nonlinear_filtering_Chen.pdf

  22. Sellers JF, Daniele CJ (1978) DYGABCD—a program for calculating linear A, B, C, and D matrices from a nonlinear dynamic engine simulation. NASA/TP-1295

  23. El Madbouly EE, Abdalla AE, El Banby GM (2009) Fuzzy adaptive Kalman filter for multi-sensor system. In: 13th international conference on aerospace sciences and aviation technology, pp 1–9

  24. Wang GY, Guan BL (2015) Fuzzy adaptive variational bayesian unscented Kalman filter. J Inf Hiding Multimed Signal Process 6(4):740–749

    Google Scholar 

  25. MICROTURBO SA (1986) TRI 60-3 propulsion systems. Manual

  26. Cohen H, Rogers G, Saravanamuttoo H (1995) Gas turbine theory, 4th edn. Longman Group Limited, London

    Google Scholar 

  27. Lagrat I, Ouakka H, Boumhidi I (2006) Fuzzy sliding mode pi controller for nonlinear systems. In: Proceedings of 6th international conference on simulation, modelling and optimization, pp 534–539

Download references

Acknowledgements

This work was supported by the 2017 Far East University Research Grant (FEU2017S01).

Author information

Authors and Affiliations

  1. Department of Aircraft Maintenance Engineering, Far East University, Eumseong County, Chungcheongbuk-do, 27601, Republic of Korea

    Dongju Han

Authors
  1. Dongju Han
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Dongju Han.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Han, D. A Study on Application of Fuzzy Adaptive Unscented Kalman Filter to Nonlinear Turbojet Engine Control. Int. J. Aeronaut. Space Sci. 19, 399–410 (2018). https://doi.org/10.1007/s42405-018-0032-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42405-018-0032-4

Keywords

Search

Navigation