Adaptive real-time neural network attitude control of chaotic satellite motion

Details Adaptive real-time neural network attitude control of chaotic satellite motion Adaptive real-time neural network attitude control of chaotic satellite motion

Apr 1995

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1995spie.2492..112k&link_type=abstract

Proc. SPIE Vol. 2492, p. 112-123, Applications and Science of Artificial Neural Networks, Steven K. Rogers; Dennis W. Ruck; Eds.

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We present a control strategy that is able to both slow down and direct the orientation of a satellite subjected to chaotic feedback forces. The experiments show that the neural-based control system is able to do this in real-time even on very inexpensive hardware such as a PC. Although no prior knowledge of the moments of inertia of the satellite are assumed, implicit knowledge of the nature of the problem is exploited in the form of a heuristic control strategy employed in the training process. The controller is adaptive to possibly changing satellite dynamics (e.g. modifications and additions to the satellite such as the one done to the Hubble telescope). The control torques produced by the controller are smooth and the system demonstrates the ability to bring the satellite into a desired orientation in the presence of large chaotic external forces in addition to noise in the sensor system. We contrast our approach to that of conventional heuristic, time-optimal and fuel-optimal control methods as well as earlier similar work using a more general neuro-genetic architecture developed by Dracopoulus and Jones.

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