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Neural control of the NASA Langley 16-foot transonic tunnel

Experimental results of controlling the Mach number in a transonic wind tunnel with a system of artificial neural networks are presented. Kohonen self-organizing maps are used to cluster the local tunnel dynamics and thereby predict the Mach number response to candidate control input sequences. The sequence minimizing the predicted error between the desired and actual Mach number is applied to the tunnel fan drive system. Comparison is made to gain scheduled automatic control currently in use.