Skip to main content
SpringerLink
Log in

A Nonlinear H∞ Set-point Control Method for Turbofan Engines with Disturbances

  • Regular Papers
  • Control Theory and Applications
  • Published:
International Journal of Control, Automation and Systems Aims and scope Submit manuscript

Abstract

To provide desired control performance for turbofan engines subject to external disturbances, a nonlinear H∞ set-point control approach is proposed in this paper. The nonlinear H∞ set-point control expands the scope of controllable operation and limits the impact of disturbances on turbofan engines, which can effectively reduce the frequency of controller switching and improve the stability of the control system. Firstly, a simple polynomial nonlinear state-space model is employed to approximate the nonlinear dynamics of turbofan engines in certain operating conditions. Then, the nonlinear H∞ set-point controller is designed to ensure the states of turbofan engines robust to exogenous disturbances. Finally, it is proven that the proposed controller guarantees asymptotic stability and robustness. Simulation results show that the proposed method can improve transient responses, disturbance rejection, surge margins, and fuel consumption for the component level engine model JT9D.

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

Similar content being viewed by others

References

  1. J. Q. Wang, G. Dimirovski, and H. Yue, “Finite-time regulation of two-spool turbofan engines with one shaft speed control,” Journal of Dynamic Systems, Measurement, and Control, vol. 138, no. 8, pp. 1–7, August 2016.

    Google Scholar 

  2. J. W. Connolly, J. T. Csank, A. Chicatelli, and K. Franco, “Propulsion controls modeling for a small turbofan engine,” Proc. of 53rd AIAA/SAE/ASEE Joint Propulsion Conference, GRC-E-DAA-TN43623, July 10, 2017.

  3. N. Wang and Y. Yang, “Turbofan engine controller optimal design based on grey wolf optimizer,” Current Trends in Computer Science and Mechanical Automation, vol. 2, pp. 458–468, December 2017.

    Google Scholar 

  4. Y. Qian, Z. Ye, H. Zhang, and L. Zhou, “LPV/PI control for nonlinear aeroengine system based on guardian maps theory,” IEEE Access, vol. 7, pp. 125854–125867, September 2019.

    Article  Google Scholar 

  5. M. Pan, K. Zhang, Y. H. Chen, and J. Huang, “A new robust tracking control design for turbofan engines: H∞/Leitmann approach,” Applied Sciences, vol. 7, no. 439, pp. 1–21, April 2017.

    Google Scholar 

  6. R. Andoga, L. Fözö, R. Kovács, K. Beneda, T. Moravec, and M. Schreiner, “Robust control of small turbojet engines,” Machines, vol. 7, no. 3, pp. 1–14, January 2019.

    Google Scholar 

  7. Q. Pan, Z. Zhang, and X. Liu, “Design of H∞ output feedback controller for gas turbine engine distributed control with random packet dropouts,” IEEE Access, vol. 7, pp. 1030–1039, January 2019.

    Article  Google Scholar 

  8. H. R. Alikhani and M. M. Motlagh, “Aero engine multivariable robust control,” Technical Journal of Engineering and Applied Sciences, vol. 5, no. 3, pp. 228–232, 2016.

    Google Scholar 

  9. K. Peng, D. Fan, F. Yang, L. Gou, and L. Wei, “A frequency domain decoupling method and multivariable controller design for turbofan engines,” IEEE Access, vol. 5, pp. 27757–27766, October 2017.

    Article  Google Scholar 

  10. Y. Wang, Q. Zheng, H. Zhang, and M. Chen, “The LQG/LTR control method for turboshaft engine with variable rotor speed based on torsional vibration suppression,” Journal of Low Frequency Noise, Vibration and Active Control, vol. 39, no. 4, pp. 1–14, May 2019.

    Google Scholar 

  11. M. Athans, P. Kapasouris, E. Kappos, and H. A. Spang, “Linear-quadratic gaussian with loop-transfer recovery methodology for the F-100 engine,” Journal of Guidance Control and Dynamics, vol. 9, no. 1, pp. 45–52, January 1986.

    Article  Google Scholar 

  12. B. J. Brunell, D. E. Viassolo, and R. Prasanth, “Model adaptation and nonlinear model predictive control of an aircraft engine,” ASME Turbo Expo 2004: Power for Land, Sea, and Air, vol. 2, pp. 673–682, June 2004.

    Article  Google Scholar 

  13. J. Wang, Z. Ye, and Z. Hu, “Nonlinear control of aircraft engines using a generalized gronwall-bellman lemma approach,” Journal of Engineering for Gas Turbines and Power, vol. 134, no. 9, pp. 1–6, July 2012.

    Article  Google Scholar 

  14. Q. Zheng, S. Pang, H. Zhang, and Z. Hu, “A study on aeroengine direct thrust control with nonlinear model predictive control based on deep neural network,” International Journal of Aeronautical and Space Sciences, vol. 2019, no. 20, pp. 933–939, July 2019.

    Article  Google Scholar 

  15. B. Yu, Y. Gao, and J. Wang, “Nonlinear control study on aircraft engine using a generalized minimum variance approach,” Advances in Mechanical Engineering, vol. 8, no. 9, pp. 1–9, August 2016.

    Article  Google Scholar 

  16. J. Bai, S. Liu, and W. Wei, “The nonlinear single controller of DGEN380 aero engine design,” International Journal of Aerospace Engineering, vol. 2019, pp. 1–12, July 2019.

    Google Scholar 

  17. H. Modares, F. L. Lewis, and Z. P. Jiang, “H∞ tracking control of completely unknown continuous-time systems via off-policy reinforcement learning,” IEEE Trans. on Neural Networks and Learning Systems, vol. 26, no. 10, pp. 2550–2562, October 2015.

    Article  MathSciNet  Google Scholar 

  18. B. Luo, D. Liu, T. Huang, and D. Wang, “Model-free optimal tracking control via critic-only Q-learning,” IEEE Trans. on Neural Networks and Learning Systems, vol. 27, no. 10, pp. 2134–2144, October 2016.

    Article  MathSciNet  Google Scholar 

  19. Y. Lv, J. Na, and X. Ren, “Online H∞ control for completely unknown nonlinear systems via an identifier-critic-based ADP structure,” International Journal of Control, vol. 92, no. 1, pp. 100–111, May 2019.

    Article  MathSciNet  Google Scholar 

  20. D. Liu, Y. Huang, and Q. Wei, “Neural network H∞ tracking control of nonlinear systems using GHJI method,” Proc. of International Symposium on Neural Networks, Berlin, Heidelberg, Springer, 2013.

    Google Scholar 

  21. Q. Liu, R. Li, Q. Zhang, and J. Li, “Adaptive robust H∞ sliding mode control for singular systems with time-varying delay and uncertain derivative matrix,” International Journal of Control, Automation and Systems, vol. 2019, no. 17, pp. 3179–3193, July 2019.

    Article  Google Scholar 

  22. Y. Pan, Y. Zhou, T. Sun, and M. J. Er, “Composite adaptive fuzzy H∞ tracking control of uncertain nonlinear systems,” Neurocomputing, vol. 99, no. 1, pp. 15–24, January 2013.

    Article  Google Scholar 

  23. C. Borsen, Y. Chang, and T. C. Lee, “Adaptive control in robotic systems with H∞ tracking performance,” Automatica, vol. 33, no. 2, pp. 227–234, February 1997.

    Article  MathSciNet  Google Scholar 

  24. Y. C. Chang, “Neural network-based H∞ tracking control for robotic systems,” IEEE Proceedings — Control Theory and Applications, vol. 147, no. 3, pp. 303–311, May 2000.

    Article  Google Scholar 

  25. Z. Yan, G. Zhang, and J. Wang, “Non-fragile robust finite-time H∞ control for nonlinear stochastic Itô systems using neural network,” Automatica, vol. 10, no. 5, pp. 873–882, June 2012.

    Google Scholar 

  26. L. Cheng, Z. G. Hou, and M. Tan, “Adaptive neural network tracking control for manipulators with uncertain kinematics, dynamics and actuator model,” Automatica, vol. 45, no. 10, pp. 2312–2318, October 2009.

    Article  MathSciNet  Google Scholar 

  27. W. Sun, S. F. Su, J. Xia, and Y. Wu, “Adaptive tracking control of wheeled inverted pendulums with periodic disturbances,” IEEE Trans. on Cybernetics, vol. 50, no. 5, pp. 1867–1876, May 2020.

    Article  Google Scholar 

  28. W. Sun, J. W. Lin, S. F. Su, N. Wang, and M. J. Er, “Reduced adaptive fuzzy decoupling control for lower limb exoskeleton,” IEEE Transactions on Cybernetics, vol. 51, no. 3, pp. 1099–1109, 2021.

    Article  Google Scholar 

  29. W. Sun, S. F. Su, Z. G. Liu, and Z. Y. Sun, “Adaptive intelligent control for input and output constrained high-order uncertain nonlinear systems,” IEEE Trans. on Systems, Man, and Cybernetics: Systems, 2019. DOI: https://doi.org/10.1109/TSMC.2019.2956063

  30. L. J. Liu, “A simple nonlinear H∞ control design method: Polynomial nonlinear control,” International Journal of Robust and Nonlinear Control, vol. 28, no. 17, pp. 5406–5423, September 2018.

    Article  MathSciNet  Google Scholar 

  31. M. Lichtsinder and Y. Levy, “Jet engine model for control and real-time simulations,” Journal of Engineering for Gas Turbines and Power, vol. 128, no. 4, pp. 745–753, October 2004.

    Article  Google Scholar 

  32. J. W. Chapman, T. M. Lavelle, and J. S. Litt, “A process for the creation of T-MATS propulsion system models from NPSS data,” Proc. of 50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, pp. 1–21, July 2014.

  33. A. M. Zinnecker, J. W. Chapman, T. M. Lavelle, and J. S. Litt, “Development of a twin-spool turbofan engine simulation using the toolbox for modeling and analysis of thermodynamic systems (T-MATS),” Proc. of 50th AIAA/ASME/SAE/ASEE Joint Propulsion Conference, pp. 1–15, May 2014.

  34. P. Gahinet, A. Nemirovski, A. J. Laub, and M. Chilali, “LMI control toolbox for use with MATLAB [software],” The MathWorks, Inc., Natick, Massachusetts, United States, 1994.

    Google Scholar 

  35. L. Ljung, “MATLAB and system identification toolbox release 2015b [software],” The MathWorks, Inc., Natick, Massachusetts, United States, 2014.

    Google Scholar 

  36. J. Wang, Z. Ye, Z. Hu, X. Wu, D. Georgi, and H. Yue, “Model-based acceleration control of turbofan engines with a Hammerstein-Wiener representation,” International Journal of Turbo & Jet-engines, vol. 34, no. 2, pp. 141–148, May 2017.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Aerospace Engineering, Xiamen University, Xiamen City, Fujian, 361005, China

    Dingding Cheng, Lijun Liu & Zhen Yu

  2. Shenzhen Research Institute of Xiamen University, Shenzhen, 518000, China

    Dingding Cheng & Lijun Liu

Authors
  1. Dingding Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lijun Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhen Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Lijun Liu or Zhen Yu.

Additional information

Publisher’s Note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This work was supported by the Guangdong Natural Science Foundation under Grant 2018A030313124, the AEAC Advanced Jet Propulsion Creativity Center Foundation under Grant HKCX2020-02-029, the Basic Research Program of Science and Technology of Shenzhen under Grant JCYJ20190809163009630, the Natural Science Foundation of Fujian under Grant 2020J01052, and the Open Foundation of Key Laboratory of Marine Navigation and Control Technology under Grant 202005.

Dingding Cheng received her B.S. degree in electrical engineering and automation and an M.S. degree in control science and engineering from Hebei University of Science and Technology, Hebei, China, in 2014 and 2017, respectively. She is currently working toward a Ph.D. degree in the School of Aerospace Engineering at Xiamen University, Fujian, China. Her current research interests include nonlinear robust control, fault diagnosis, and fault tolerant control.

Lijun Liu received his B.S. degree in mathematics from Jilin University in 2007, and his M.E. and Ph.D. degrees in control science and engineering from Harbin Institute of Technology, in 2009 and 2013, respectively. Since January 2013, he has been an Assistant Professor with the Department of Automation at Xiamen University. His current research interests include fault diagnosis and fault tolerant control, flight control, and computational electromagnetics and acoustics.

Zhen Yu received his B.S. and M.S. degrees from Harbin Engineering University and his Ph.D. degree from Xiamen University, in 1985, 1988, and 2009, respectively. Since 2009, he has been a Professor with the Department of Automation at Xiamen University. From 2014 to 2015, he has been a visiting scholar at Louisiana State University. His research interests include control engineering, fault diagnosis and fault-tolerant control.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cheng, D., Liu, L. & Yu, Z. A Nonlinear H∞ Set-point Control Method for Turbofan Engines with Disturbances. Int. J. Control Autom. Syst. 19, 3062–3074 (2021). https://doi.org/10.1007/s12555-020-0436-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12555-020-0436-3

Keywords

Search

Navigation