Skip to main content

Worst Case Analysis of Control Law for Re-entry Vehicles Using Hybrid Differential Evolution

  • Chapter
Advances in Differential Evolution

Part of the book series: Studies in Computational Intelligence ((SCI,volume 143))

Summary

The development and application of the differential evolution (DE) optimisation algorithm to the problem of worst-case analysis of nonlinear control laws for hypersonic re-entry vehicles is described. The algorithm is applied to the problem of evaluating a proposed nonlinear handling qualities clearance criterion for a detailed simulation model of a hypersonic re-entry vehicle (also known as a reusable launch vehicle (RLV)) having a full-authority nonlinear dynamic inversion (NDI) flight control law. A hybrid version of the differential evolution algorithm, incorporating local gradient-based optimisation, is also developed and evaluated. Comparisons of computational complexity and global convergence properties reveal the significant benefits which may be obtained through hybridisation of the standard differential evolution algorithm. The proposed optimisation-based approach to worst-case analysis is shown to have significant potential for improving both the reliability and efficiency of the flight clearance process for next generation RLV’s.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Fielding, C., Varga, A., Bennani, S., Selier, M. (eds.): Advanced techniques for clearance of flight control laws. Springer, Heidelberg (2002)

    MATH  Google Scholar 

  2. Menon, P.P., Kim, J., Bates, D.G., Postlethwaite, I.: Improved Clearance of Flight Control Laws Using Hybrid Optimisation. In: Proc. of the IEEE Conference on Cybernetics and Intelligent Systems, Singapore (December 2004)

    Google Scholar 

  3. Forssell, L.S., Hovmark, G., Hyden, Å., Johansson, F.: The aero-data model in a research environment (ADMIRE) for flight control robustness evaluation, GARTUER/TP-119-7 (August 1, 2001), http://www.foi.se/admire/main.html

  4. Rundqwist, L., Stahl-Gunnarsson, K., Enhagen, J.: Rate limiters with phase compensation in JAS39 GRIPEN. In: Proc. of the European Control Conference, July 1997, pp. 2451–2457 (1997)

    Google Scholar 

  5. Forssell, L.S., Hyden, Å.: Flight control system validation using global nonlinear optimisation algorithms. In: Proc. of the European Control Conference, Cambridge, U.K. (September 2003)

    Google Scholar 

  6. Forssell, L.S.: Flight clearance analysis using global nonlinear optimisation based search algorithms. In: Proc. of the AIAA Guidance, Navigation, and Control Conference, Austin, Texas (August 2003)

    Google Scholar 

  7. Optimization toolbox users guide, Version 2, The MathWorks (September 2000)

    Google Scholar 

  8. Back, T., Hammel, U., Schwefel, H.P.: Evolutionary computation: comments on the history and current state. IEEE Transactions on Evolutionary Computation 1(1), 3–17 (1997)

    Article  Google Scholar 

  9. Fleming, P.J., Purshouse, R.C.: Evolutionary algorithms in control systems engineering: a survey. Control Engineering Practice 10, 1223–1241 (2002)

    Article  Google Scholar 

  10. Rogalsky, T., Derksen, R.W., Kocabiyik, S.: Differential evolution in aerodynamic optimization. Canadian Aeronautics and Space Institute Journal 46, 183–190 (2000)

    Google Scholar 

  11. Storn, R., Price, K.: Differential evolution: a simple and efficient heuristic for global optimization over continuous space. Journal of Global Optimization 11, 341–369 (1997)

    Article  MATH  MathSciNet  Google Scholar 

  12. Clerc, M., Kennedy, J.: The particle swarm - explosion, stability, and convergence in a multidimensional complex space. IEEE Transactions on Evolutionary Computation 6(1), 58–73 (2002)

    Article  Google Scholar 

  13. Dorigo, M., Gambardella, L.M.: Ant colony system: a cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation 1(1), 53–66 (1997)

    Article  Google Scholar 

  14. Zelinka, I., Lampinen, J.: SOMA - self-organizing migrating algorithm. In: 6th International Conference on Soft Computing, Brno, Czech Republic (2000) ISBN 80-214-1609-2

    Google Scholar 

  15. Goldberg, D.E.: Genetic algorithms in search, optimization and machine learning. Addison-Wesley, Reading (1989)

    MATH  Google Scholar 

  16. Davis, L. (ed.): Handbook of genetic algorithms. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  17. Lampinen, J., Zelinka, I.: Mechanical engineering design by differential evolution. In: Corne, D., Dorigo, M., Glover, F. (eds.) New Ideas in Optimisation, pp. 127–146. McGraw-Hill, London (1999)

    Google Scholar 

  18. Storn, R.: System design by constraint adaptation and differential evolution. IEEE Transactions on Evolutionary Computation 3(1), 22–34 (1999)

    Article  Google Scholar 

  19. Yen, J., Liao, J.C., Randolph, D., Lee, B.: A hybrid approach to modeling metabolic systems using genetic algorithm and simplex method. In: Proc. of the 11th IEEE Conference on Artificial Intelligence for Applications, Los Angeles, CA, Feburary 1995, pp. 277–283 (1995)

    Google Scholar 

  20. Lobo, F.G., Goldberg, D.E.: Decision making in a hybrid genetic algorithm, IlliGAL Report No. 96009 (September 1996)

    Google Scholar 

  21. Krasnogor, N., Smith, J.: A tutorial for competent memetic algorithms: model, taxonomy, and design issues. IEEE Transactions on Evolutionary Computation 9(5), 474–488 (2005)

    Article  Google Scholar 

  22. Rogalsky, T., Derksen, R.W.: Hybridization of differential evolution for aerodynamic design. In: Proc. of the 8th Annual Conference of the Computational Fluid Dynamics Society of Canada, pp. 729–736 (2000)

    Google Scholar 

  23. FDI Test Bench Software User Manual, FDITB-DME-SUM, Version 1.1 (September 18, 2006)

    Google Scholar 

  24. Menon, P.P., Bates, D.G., Postlethwaite, I.: Robustness Analysis of Nonlinear Flight Control Laws over Continuous Regions of the Flight Envelope. In: Proceedings of the IFAC Symposium on Robust Control Design (July 2006)

    Google Scholar 

  25. Menon, P.P., Kim, J., Bates, D.G., Postelthwaite, I.: Clearance of nonlinear flight control laws using hybrid evolutionary optimisation. IEEE Transactions on Evolutionary Computation 10(6), 689–699 (2006)

    Article  Google Scholar 

  26. Menon, P.P., Bates, D.G., Postlethwaite, I.: A Deterministic Hybrid Optimisation Algorithm for Nonlinear Flight Control Systems Analysis. In: Proceedings of the American Control Conference (June 2006)

    Google Scholar 

  27. Madavan, N.: Aerodynamic shape optimisation using hybrid differential evolution, AIAA-2003-3792, 21st AIAA Applied aerodynamic conference, Orlando, Florida, USA (2003)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Uday K. Chakraborty

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Menon, P.P., Bates, D.G., Postlethwaite, I., Marcos, A., Fernandez, V., Bennani, S. (2008). Worst Case Analysis of Control Law for Re-entry Vehicles Using Hybrid Differential Evolution. In: Chakraborty, U.K. (eds) Advances in Differential Evolution. Studies in Computational Intelligence, vol 143. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68830-3_14

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-68830-3_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-68827-3

  • Online ISBN: 978-3-540-68830-3

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics