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Adaptive neuro-genetic control of chaos applied to the attitude control problem

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Abstract

Conventional adaptive control techniques have, for the most part, been based on methods for linear or weakly non-linear systems. More recently, neural network and genetic algorithm controllers have started to be applied to complex, non-linear dynamic systems. The control of chaotic dynamic systems poses a series of especially challenging problems. In this paper, an adaptive control architecture using neural networks and genetic algorithms is applied to a complex, highly nonlinear, chaotic dynamic system: the adaptive attitude control problem (for a satellite), in the presence of large, external forces (which left to themselves led the system into a chaotic motion). In contrast to the OGY method, which uses small control adjustments to stabilize a chaotic system in an otherwise unstable but natural periodic orbit of the system, the neuro-genetic controller may use large control adjustments and proves capable of effectively attaining any specified system state, with no a prioriknowledge of the dynamics, even in the presence of significant noise.

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Authors and Affiliations

  1. Department of Computer Science, Brunel University, UB8 3PH, London, Uxbridge, Middlesex, UK

    D. C. Dracopoulos & A. J. Jones

  2. Department of Computer Science, University of Wales, Cardiff, UK

    D. C. Dracopoulos & A. J. Jones

Authors
  1. D. C. Dracopoulos
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  2. A. J. Jones
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This work was partly supported by SERC grant 90800355.

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Dracopoulos, D.C., Jones, A.J. Adaptive neuro-genetic control of chaos applied to the attitude control problem. Neural Comput & Applic 6, 102–115 (1997). https://doi.org/10.1007/BF01414007

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