Skip to main content
Log in

Pressure regulation inside a hypersonic wind tunnel using H-infinity optimization control

  • Published:
Automatic Control and Computer Sciences Aims and scope Submit manuscript

Abstract

Hypersonic wind tunnel is a ground-based facility used to study the aerodynamic properties of space vehicles during re-entry. This paper aims at designing an H-infinity controller with krill herd optimization algorithm to regulate pressure inside the settling chamber of a hypersonic wind tunnel. The krill herd algorithm is a novel stochastic algorithm for improving the performance characteristics by optimizing the H-infinity controller parameters. The proposed algorithm minimizes the H-infinity norms by tuning the controller weighing function parameters. The dynamic characteristics of the settling chamber pressure with H-infinity and H-infinity control based on krill herd algorithm is studied by numerical simulations. The proposed algorithm is highly efficient and robust in controlling the settling chamber pressure in terms of performance parameters.

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.

Institutional subscriptions

Similar content being viewed by others

References

  1. Nott, C.R., Ölçmen, S.M., Lewis, D.R., and Williams, K., Supersonic, variable-throat, blow-down wind tunnel control using genetic algorithms, neural networks, and gain scheduled PID, Appl. Intell., Springer, 2008, vol. 29, pp. 79–89.

    Article  Google Scholar 

  2. Savino, R., Monti, R., Esposito, A., Lewis, D.R., and Williams, K., Behaviour of hypersonic wind tunnels diffusers at low Reynolds numbers, Aerospace Sci. Technol., 2009, vol. 3, no. 1, pp. 11–19.

    Article  MATH  Google Scholar 

  3. Rajani, S.H., Krishna, B.M., and Nair, U., Stability analysis and temperature effect on the settling chamber pressure of a hypersonic wind tunnel, in Proceedings of IEEE International Conference on Computational Intelligence and Computing Research, 2012.

    Google Scholar 

  4. Jacob, V. and Binu, L.S., Adaptive fuzzy PI controller for hypersonic wind tunnel pressure regulation, Proceedings of 10th National Conference on Technological Trends (NCTT), 2009, pp. 184–187.

    Google Scholar 

  5. Yang Ji Lee, Sang Hun Kang, Soo Seok Yang, and Se Jin Kwon, Starting characteristics of the hypersonic wind tunnel with the much number variation, J. Mech. Sci. Technol., 2014, vol. 28, no. 6, pp. 2197–2204.

    Article  Google Scholar 

  6. Braun, E.M., Lu, F.K., Panicker, P.K., Mitchell, R.R., and Wilson, D.R., Supersonic blowdown wind tunnel control using LabVIEW, 46th AIAA Aerospace Sciences Meeting and Exhibit, Reno, Nevada, 2008.

    Google Scholar 

  7. Rini Jones, S.B., Poongodi, P., and Binu, L.S, Fuzzy assisted PI controller for pressure regulation in a hypersonic wind tunnel, Int. J. Hybrid Inf. Technol., 2011, vol. 4, no. 1, pp. 13–24.

    Google Scholar 

  8. Bottasso, C.L., Campagnolo, F., and Petrovic, V., Wind tunnel testing of scaled wind turbine models: Beyond aerodynamics, J. Wind Eng. Ind. Aerodyn., 2014, vol. 127, pp. 11–28.

    Article  Google Scholar 

  9. Bhoi, S.R. and Suryanarayana, G.K., Prediction of total pressure characteristics in the settling chamber of a supersonic blowdown wind tunnel, Proceedings of the International Conference on Aerospace Science and Technology, Bangaloire, 2008, pp. 26–28.

    Google Scholar 

  10. Hwang, D.S. and Hsu, P.L., A robust controller design for supersonic intermittent blow down type wind tunnels, Aeronaut. J. R. Aeronaut. Soc., 1998, vol. 102, no. 1013, pp. 161–169.

    Google Scholar 

  11. Rini Jones, S.B., Poongodi, P., and Binu, L.S., Fuzzy assisted PI controller with anti-reset wind up for regulating pressure in a hypersonic wind tunnel, IJCA Special Issue on Artificial Intelligence Techniques-Novel Approaches & Practical Applications, 2011, pp. 29–33.

    Google Scholar 

  12. Rajani, S.H. and Nair, U., Design and analysis of a nonlinear controller for a hypersonic wind tunnel, Proceedings of IEEE International Conference on Computational Intelligence and Computing Research, 2013, pp. 106–109.

    Google Scholar 

  13. Rajani, S.H., Krishna, B.M., and Nair, U., Design and analysis of H-infinity controller for a hypersonic wind tunnel, Proceedings of Elsevier on International Conference on Control Systems and Power Electronics, 2012.

    Google Scholar 

  14. Dolly Mary, A., Mathew, A.T., and Jacob, J., Robust H-infinity (H∞) stabilization of uncertain wheeled mobile robots, Global J. Res. Eng. Electr. Electron. Eng., 2012, vol. 12, no. 1.

    Google Scholar 

  15. Yilmaz, M., Mujeeb, S., and Dhansri, N.R., A H-infinity control approach for oil drilling processes, Procedia Comput. Sci., 2012, vol. 20, pp. 134–139.

    Article  Google Scholar 

  16. Hassibi, B., Erdogan, A.T., and Kailath, T., MIMO linear equalization with an H-infinity criterion, IEEE Trans. Signal Process., 2006, vol. 54, no 9, pp. 499–511.

    Article  MATH  Google Scholar 

  17. Vikalo, H., Hassibi, B., Erdogan, A.T., and Kailath, T., On H-infinity design techniques for robust signal reconstruction in noisy filter banks, EURASIP Signal Process., 2005, vol. 85, no. 1, pp. 1–14.

    Article  MATH  Google Scholar 

  18. Wang, G.-Ge, Gandomi, A.H., and Alavi, A.H., An effective krill herd algorithm with migration operator in biogeography-based optimization, Appl. Math. Model., 2014, vol. 38, pp. 2454–2462.

    Article  MathSciNet  Google Scholar 

  19. Wu, P., Gao, L., Zou, D., et al., An improved particle swarm optimization algorithm for reliability problems, ISA Trans., 2011, vol. 50, no. 1, pp. 71–81.

    Article  Google Scholar 

  20. Gandomi, A.H. and Alavi, A.H., Krill herd: A new bio-inspired optimization algorithm, Commun. Nonlinear Sci. Numer. Simulat., 2012, vol. 17, no. 12, pp. 4831–4845.

    Article  MathSciNet  MATH  Google Scholar 

  21. Yang, X.S. and Gandomi, A.H., Bat algorithm: A novel approach for global engineering optimization, Eng. Comput., 2012, vol. 29, no. 5, pp. 464–483.

    Article  Google Scholar 

  22. Alfi, A. and Modares, H., System identification and control using adaptive particle swarm optimization, Appl. Math. Model., 2011, vol. 35, no. 3, pp. 1210–1221.

    Article  MathSciNet  MATH  Google Scholar 

  23. Ali, H.I., Noor, S.B.M., Bashi, S.M., and Marhaban, M.H., Design of H-infinity based robust control algorithms using particle swarm optimization method, Mediterr. J. Measur. Control, 2010, vol. 6, no. 2.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. H. Rajani.

Additional information

The article is published in the original.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rajani, S.H., Krishna, B.M. & Nair, U. Pressure regulation inside a hypersonic wind tunnel using H-infinity optimization control. Aut. Control Comp. Sci. 51, 399–409 (2017). https://doi.org/10.3103/S0146411617060074

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.3103/S0146411617060074

Keywords

Navigation