Abstract
The design of online adaptive neural networks for use in a nonlinear helicopter e ight control architecture is treated. Emphasis is given to network architecture and the effect that varying the adaptation gain has on performance. Conclusionsarebasedon asix-degree-of-freedom nonlinearevaluation model ofan attackhelicopter and ametricthatmeasuresthenetwork’ sability tocancel theeffectofmodeling errorsforacomplicated maneuver. Thenetwork isshownto providenearlyperfecttracking in thefaceofsignie cantmodelingerrorsand,additionally, to cancel the model inversion error after a short initial period of learning. Furthermore, it is shown that the performance varies gracefully and monotonically improves as the adaptation gain parameter is increased. The effect on control effort is modest and is mainly perceptible only during a short training episode that can be associated with transition from hover to forward e ight.
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