Genetic Algorithms Training of Neural Nets for Aircraft Guidance and Control
نویسندگان
چکیده
As airframe become more and more complex, and are called upon to perform increasingly stre~sful maneuvers, autopilots must be robust enough to adequately stabilize the airframe in the highly non-linear, strongly cross-coupled environments. Classic autopilot design can achieve stability throughout he flight envelope, but generally lack robustness for design and environmental ehange.~. Guidance and control routines composed of a neural net architecture offer a promising ability to process multiple inputs, generate the appropriate outputs, and provide greater robustness. However, difficulty can arise in the training proce,~ of the neural nets. In the present study, a feedforward neural net wa~ used for the guidance and control routine.~ on typical airframe.~. The neural nets were trained through genetic algorithms. The work attempt~ to model the biological process of the "’thinking" aspect of the airframe.~ by the u~ of a neural nets trained through natural ~lection a~ put forth in the Theory of Evolution. The pre~nt study produced an autopilot that learned to control it~ rates and maneuver (with a full six degree.~-of-freedom) across an arena to a target.
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