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Neural networks and genetic algorithms for the attitude control problem

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From Natural to Artificial Neural Computation (IWANN 1995)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 930))

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Abstract

A general adaptive control method using genetic algorithms and neural networks is proposed and applied to a highly nonlinear problem, the attitude control problem. Examples are given where the method successfully control a rigid body satellite with unknown dynamics, including an example where the satellite is subject to external forces trying to lead it into a chaotic motion.

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References

  1. Dimitris C. Dracopoulos and Antonia J. Jones. Modeling dynamic systems. In 1st World Congress on Neural Networks Proceedings. INNS/Erlbaum Press, 1993.

    Google Scholar 

  2. Dimitris C. Dracopoulos and Antonia J. Jones. Neuromodels of analytic dynamic systems. Neural Computing & Applications, 1(4):268–279, 1993.

    Google Scholar 

  3. David E. Goldberg. Genetic Algorithms in Search, Optimization and Machine Learning. Addjson Wesley, 1989.

    Google Scholar 

  4. Herbert Goldstein. Classical Mechanics. Addison Wesley, second edition, 1980.

    Google Scholar 

  5. John J. Greffenstette. Optimization of control parameters for genetic algorithms. IEEE Transactions on Systems, Man and Cybernetics, SMC-16:122–128, 1986.

    Google Scholar 

  6. R. B. Leipnik and T. A. Newton. Double strange attractors in rigid body motion with linear feedback control. Physics Letters, 86A:63–67, 1981.

    Google Scholar 

  7. George Meyer. On the use of Euler's theorem on rotations for the synthesis of attitude control systems. Technical Report TN D-3643, NASA, 1966.

    Google Scholar 

  8. Francis C. Moon. Chaotic and Fractal Dynamics. John Wiley and Sons, 1992.

    Google Scholar 

  9. E. Ott, C. Grebogi, and James Yorke. Controlling chaos. Physical Review Letters, 64(11), 1990.

    Google Scholar 

  10. George E. Piper and Harry G. Kwatny. Complicated dynamics in spacecraft attitude control systems. Journal of Guidance, Control and Dynamics, 15(4):825–831, July–August 1992.

    Google Scholar 

  11. David Rumelhart, James McClelland, and the PDP research group. Parallel Distributed Processing-Explorations in the Microstructure Cognition, volume 1. MIT Press, 1986.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, Brunel University, London, UK

    Dimitris C. Dracopoulos

  2. Department of Computing, Imperial College, London, UK

    Antonia J. Jones

Authors
  1. Dimitris C. Dracopoulos
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  2. Antonia J. Jones
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José Mira Francisco Sandoval

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© 1995 Springer-Verlag Berlin Heidelberg

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Dracopoulos, D.C., Jones, A.J. (1995). Neural networks and genetic algorithms for the attitude control problem. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_191

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  • DOI: https://doi.org/10.1007/3-540-59497-3_191

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-59497-0

  • Online ISBN: 978-3-540-49288-7

  • eBook Packages: Springer Book Archive

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