Skip to main content
Log in

Fuzzy Sliding Mode Predictive Control of Air Flow Rate for a High-Speed High-Temperature Heat-Airflow Test System

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

Abstract

To solve the problem of accurate control of air flow rate of a high-speed high-temperature heat-airflow test system, this paper introduces the working principle, establishes the mathematical model and analyzes the characteristics of the air supply subsystem. According to the characteristics of the air supply subsystem, such as time-varying parameters, time delay and disturbance, which are difficult to control accurately, a new fuzzy sliding mode predictive control algorithm is proposed based on fuzzy sliding mode control and Smith predictor. On this basis, the proposed control algorithm is simulated and studied. The results show that the proposed fuzzy sliding mode predictive control algorithm has good control performance. It can not only achieve high-performance tracking of step, slope, square wave and sinusoidal signals, but also can overcome the influence on the system caused by pure time delay, time-varying parameter and external disturbance.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Huang Q (2017) Future research of the hypersonic and space vehicle technology. Sci Technol Inf 15(31):76–77, 79

  2. Cai C, Li Y, Dong S (2016) Experimental study on control of gas temperature for a high-speed heat-airflow wind tunnel. J Aerosp Eng 29(6):04016054

    Google Scholar 

  3. Li Y, Cai C, Lee KM, Teng F (2013) A novel cascade temperature control system for a high-speed heat-airflow wind tunnel. IEEE/ASME Trans Mechatron 18(4):1310–1319

    Google Scholar 

  4. Cai C, Yang Y, Liu T (2016) Coordinated control of fuel flow-rate for a high-temperature high-speed wind tunnel. Proc Inst Mech Eng G J Aerosp Eng 230(13):2504–2514

    Google Scholar 

  5. Cai C, Li Y (2014) Undisturbed switching control of fuel flow-rate for a high-speed heat-airflow wind tunnel. Proc Inst Mech Eng G J Aerosp Eng 228(12):2245–2254

    Google Scholar 

  6. Akbar A, Saputra C, Munir MM, Khairurrijal (2016) Design and implementation of automatic air flow rate control system. J Phys Conf Ser 739:012011

    Google Scholar 

  7. Hao L, Qiao Z, Zhang Y, Gao C, Wu J (2010) Design research on the Mach number closed-loop control system in the NF-6 wind tunnel. J Exp Fluid Mech 24(4):85–88

    Google Scholar 

  8. Chu W, Tang G, Wang F (2012) Research and realization on the control strategies of the 2 m × 2 m supersonic wind tunnel. J Exp Fluid Mech 26(5):98–102

    Google Scholar 

  9. Gao C, Liu F, Zhou B, Zhou R, Yu B (2013) Measure and control system of the supersonic wind tunnel. Ordnance Ind Autom 32(2):63–66

    Google Scholar 

  10. Zhao S, Shi H, Leng C, Lv Q (2004) Research of application of neural network in Mach number identification of wind tunnel flow field. J Exp Fluid Mech 18(3):87–91

    Google Scholar 

  11. Jin Z, Yang X, Su B (2016) Predictive control simulation research of Mach number in wind tunnel based on neural network. Ordnance Ind Autom 35(3):59–61

    Google Scholar 

  12. Yang H, Zhang W, Luo C, Rong X, Jici Z (2015) Application of fuzzy control in wind tunnel main airflow pressure auto-adjust system. Ordnance Ind Autom 34(4):39–42

    Google Scholar 

  13. Cheng Z, Mao Z, Du S, Sun Q, Yuan Z (2003) Analysis of robustness of PID-GPC based on IMC structure. Chin J Chem Eng 11(1):55–61

    Google Scholar 

  14. Lou G, Tan W, Fang F (2010) Control structure analysis and design for boiler-turbine units. In: Chen J (ed) 29th Chinese control conference. IEEE, Beijing, pp 4958–4963

    Google Scholar 

  15. Han M, Han B, Xi J, Hirasawa K (2006) Universal learning net-work and its application for nonlinear system with long time delay. Comput Chem Eng 31(1):13–20

    Google Scholar 

  16. Shi Y, Wang J, Zhang Y (2012) Sliding mode predictive control of main steam pressure in coal-fired power plant boiler. Chin J Chem Eng 20(6):1107–1112

    Google Scholar 

  17. Barambones O, Alkorta P (2014) Position control of the induction motor using an adaptive sliding-mode controller and observers. IEEE Trans Ind Electron 61(12):6556–6565

    Google Scholar 

  18. Galvan-Guerra R, Fridman L (2015) Static output feedback sliding mode control design via an artificial stabilizing delay. IET Control Theory Appl 9(4):563–572

    MathSciNet  Google Scholar 

  19. Li S, Du H, Yu X (2014) Discrete-time terminal sliding mode control systems based on Euler’s discretization. IEEE Trans Autom Control 59(2):546–552

    MathSciNet  MATH  Google Scholar 

  20. Xu Z (2005) Research on fuzzy control of electro-hydraulic position servo system. Kunming University of Science and Technology, Kunming

    Google Scholar 

  21. Cheng J, Liu W, Zhang Z (2011) Modeling and simulation for the electro-hydraulic servo system based on Simulink. IEEE consumer electronics, communications and networks (CECNet), April 16–18, Langtang, pp 466–469

  22. Jia W, Yin C, Li G, Sun M (2017) Modeling and stability of electro-hydraulic servo of hydraulic excavator. IOP Conf Ser Earth Environ Sci 94:012013

    Google Scholar 

  23. Sanville F (1971) A new method of specifying the flow capacity of pneumatic fluid power valves. Hydraul Pneum Power 17:120–126

    Google Scholar 

  24. Wang J, Rad AB, Chen PT (2001) Indirect adaptive fuzzy sliding mode control: part I: fuzzy switching. Fuzzy Set Syst 122:21–30

    MATH  Google Scholar 

Download references

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by the Nature Science Foundation of Hebei Province Grant no. E2017402037, and science and technology research project of Hebei Province, Grant no. ZD2018012.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chaozhi Cai.

Ethics declarations

Conflict of interest

The authors declare that there is no conflict of interests regarding the publication of this paper.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cai, C., Guo, L. & Liu, J. Fuzzy Sliding Mode Predictive Control of Air Flow Rate for a High-Speed High-Temperature Heat-Airflow Test System. Int. J. Aeronaut. Space Sci. 21, 806–815 (2020). https://doi.org/10.1007/s42405-020-00256-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s42405-020-00256-9

Keywords

Navigation