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An online implementable differential evolution tuned optimal guidance law

Published:07 July 2007Publication History

ABSTRACT

This paper proposes a novel application of differential evolution to solve a difficult dynamic optimisation or optimal control problem. The miss distance in a missile-target engagement is minimised using differential evolution. The difficulty of solving it by existing conventional techniques in optimal control theory is caused by the nonlinearity of the dynamic constraint equation, inequality constraint on the control input and inequality constraint on another parameter that enters problem indirectly.

The optimal control problem of finding the minimum miss distance has an analytical solution subject to several simplifying assumptions. In the approach proposed in this paper, the initial population is generated around the seed value given by this analytical solution. Thereafter, the algorithm progresses to an acceptable final solution within a few generations, satisfying the constraints at every iteration. Since this solution or the control input has to be obtained in real time to be of any use in practice, the feasibility of online implementation is also illustrated.

References

  1. Linkens, D. A., and Nyongesa, H. O., "Genetic algorithms for fuzzy control part 2; online system development and application", IEE Proc. -- Control Theory Appl., Vol. 142, No. 3, May 1995.Google ScholarGoogle Scholar
  2. Zarchan P., Tactical and Strategic Missile Guidance, Progress in Astronautics and Aeronautics, Vol. 199, Fourth Ed., AIAA, Reston, Virginia, 2002.Google ScholarGoogle Scholar
  3. Chen, B. S., Chen, Y. Y., and Lin, C. L., 'Nonlinear Fuzzy H∞ Guidance Law With Saturation of Actuators Against Maneuvering Targets', IEEE Transactions on Control Systems Technology, vol. 10, no. 6, Nov. 2002, pp. 769--779.Google ScholarGoogle ScholarCross RefCross Ref
  4. J. Shinar, and T. Shima, 'Guidance Law Evaluation in Highly Nonlinear Scenarios -- Comparison to Linear Analysis,' in AIAA Guidance, Navigation and Control Conference, 1999, Paper no. 99--4065, pp. 651--661.Google ScholarGoogle Scholar
  5. Thangavelu. R. , and Pradeep, S., 'A Differential Evolution Tuned Optimal Guidance Law,' in Mediterannean Conference on Control and Automation, June 2007, Paper no. T13--002.Google ScholarGoogle Scholar
  6. D. S. Naidu, Optimal Control Systems, CRC Press LLC, Boca Raton, Florida, 2003 Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. John T. Betts, Practical Methods for Optimal Control Using Nonlinear Programming, Society for Industrial and Applied Mathematics, Philadelphia, 2001, pp. 84. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. H. Seywald, R. R. Kumar, and S. M. Deshpande, "Genetic Algorithm Approach for Optimal Control Problems with Linearly Appearing Controls," Journal of Guidance, Control, and Dynamics, Vol. 18, No. 1, Jan.--Feb. 1995, pp. 177--182.Google ScholarGoogle Scholar
  9. Differential Evolution home page. Available: http://www.icsi.berkeley.edu/~storn/code.htmlGoogle ScholarGoogle Scholar
  10. Y. C. Sim, S. B. Leng, and V. Subramaniam, "A combined genetic algorithms-shooting method approach to solving optimal control problems," International Journal of Systems Science, Vol 31, No. 1, 2000, pp. 83--89.Google ScholarGoogle Scholar

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              cover image ACM Conferences
              GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
              July 2007
              2313 pages
              ISBN:9781595936974
              DOI:10.1145/1276958

              Copyright © 2007 ACM

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              Publication History

              • Published: 7 July 2007

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              GECCO '07 Paper Acceptance Rate266of577submissions,46%Overall Acceptance Rate1,669of4,410submissions,38%

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