Skip to main content
SpringerLink
Log in

Adaptive recurrent-functional-link-network control for hypersonic vehicles with atmospheric disturbances

  • Research Papers
  • Special Focus
  • Published:
Science China Information Sciences Aims and scope Submit manuscript

Abstract

The controller design for a near-space hypersonic vehicle (NHV) is challenging due to its plant uncertainties and sensitivity to atmospheric disturbances such as gusts and turbulence. This paper first derives 12 states equations of NHVs subjected to variable wind field, and presents a novel recurrent neural network (RNN) control method for restraining atmospheric disturbances. The method devises a new B-spline recurrent functional link network (BRFLN) and combines it with the nonlinear generalized predictive control (NGPC) algorithm. Moreover, the proportional-derivative (PD) correction BRFLN is proposed to approximate atmospheric disturbances in flight. The weights of BRFLN are online tuned by the adaptive law based on Lyapunov stability theorem. Finally, simulation results show a satisfactory performance for the attitude tracking of the NHV in the mesosphere, and also illustrate the controller’s robustness to wind turbulence.

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. Xu H J, Mirmirani M D, Ioannou P A. Adaptive sliding mode control design for a hypersonic flight vehicle. J Guid Control Dynam, 2004, 27: 829–838

    Article  Google Scholar 

  2. Poulain F, Piet-Lahanier H, Serre L. Nonlinear control of an air-breathing hypersonic vehicle. In: Proceedings of AIAA International Space Planes and Hypersonic Systems and Technologies Conference, Bremen, AIAA 2009-7290. 2009. 1–14

  3. Parker J T, Serrani A, Yurkovich S, et al. Approximate feedback linearization of an air-breathing hypersonic vehicle. In: Proceedings of AIAA Guidance, Navigation, and Control Conference and Exhibit, Keystone, AIAA 2006-6556. 2006. 1–16

  4. Fiorentini L, Serrani A. Nonlinear robust adaptive control of flexible air-breathing hypersonic vehicles. J Guid Control Dynam, 2009, 32: 401–415

    Article  Google Scholar 

  5. Buschek H, Calise A J. Uncertainty modeling and fixed-order controller design for a hypersonic vehicle model. J Guid Control Dynam, 1997, 20: 42–47

    Article  Google Scholar 

  6. Wilcox Z D, MacKunis W, Bhat S, et al. Robust nonlinear control of a hypersonic aircraft in the presence of aerothermoelastic effects. In: Proceedings of 2009 American Control Conference, Lt. Louis, 2009. 2533–2538

  7. Du Y L, Wu Q X, Jiang C S, et al. Adaptive functional link network control of near-space vehicle with dynamical uncertainties. J Syst Eng Electr, 2010, 21: 868–876

    Google Scholar 

  8. Johnson D L. Terrestrial environment (climatic) criteria guidelines for use in aerospace vehicle development (2008 Revision). NASA/TM-2008-215633. 2008

  9. Chen WH, Balance D J, Gawthrop P J. Optimal control of nonlinear systems: a predictive control approach, Automatica, 2003, 39: 633–641

    Article  MATH  Google Scholar 

  10. Pao Y H. Neural-net computing and intelligent control systems. Int J Control, 1992, 56: 263–289

    Article  MathSciNet  MATH  Google Scholar 

  11. Toh K A, Yau W Y. Fingerprint and speaker verification decisions fusion using functional link artificial neural networks. IEEE Trans Syst Man Cybern -Part C, 2005, 35: 357–370

    Article  Google Scholar 

  12. Zhao H Q, Zhang J S. Adaptively combined FIR and functional link artificial neural network equalizer for nonlinear communication channel. IEEE Trans Neural Netw, 2009, 20: 665–674

    Article  Google Scholar 

  13. Colgren R, Keshmiri S, Mirmirani M. Nonlinear ten-degree-of-freedom dynamics model of a generic hypersonic vehicle. J Aircraft, 2009, 46: 800–813

    Article  Google Scholar 

  14. Xiao Y L, Jin C J. Flight Principle in Atmospheric Disturbance (in Chinese). Beijing: Defense Industry Press, 1993. 73–78

    Google Scholar 

  15. Isidori A. Nonlinear Control Systems (in Chinese). 3rd ed. Beijing: Publishing House of Electronics Industry, 1995. 172–173

    Google Scholar 

  16. Wu B, Wu K, Lü J H. A novel compensation-based recurrent fuzzy neural network and its learning algorithm. Sci China Ser F-Inf Sci, 2009, 52: 41–51

    Article  MATH  Google Scholar 

  17. Samadi S, Ahmad M O, Swamy M N S. Characterization of B-spline digital filters. IEEE Trans Circ Syst, 2004, 51: 808–816

    Article  MathSciNet  Google Scholar 

  18. Polycarpou M M. Stable adaptive neural control scheme for nonlinear systems. IEEE Trans Automat Control, 1996, 41: 447–451

    Article  MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. College of Astronautics, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    YanLi Du

  2. College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    QingXian Wu, ChangSheng Jiang & YaLi Xue

Authors
  1. YanLi Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. QingXian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. ChangSheng Jiang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. YaLi Xue
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to YanLi Du.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Du, Y., Wu, Q., Jiang, C. et al. Adaptive recurrent-functional-link-network control for hypersonic vehicles with atmospheric disturbances. Sci. China Inf. Sci. 54, 482–497 (2011). https://doi.org/10.1007/s11432-011-4186-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11432-011-4186-y

Keywords

Search

Navigation