This work presents an adaptive neural network approach for the identification of a model for the control of the vibration in a Blackhawk helicopter. A feedforward neural network is used to identify a nonlinear model relating the higher harmonic blade pitch motion to the vibration state. An optimal linearization approach is applied at every operating point of interest to obtain a linear model that is locally equivalent to the neural network model at that particular operating point. This linear model can be used to obtain optimal vibration control commands. The data used for development of the model were obtained during the wind tunnel testing of the Blackhawk (UH-60A) rotor in the NASA 10-by-120-foot wind tunnel. Two different modeling approaches were used, linear quasi-static and state-space. The proposed neural-network method of system identification was compared to the least squares method. This comparison showed that the neural network consistently produced lower identification errors. The results obtained also reveal that the approximation performance improves when both the coefficients of the blade pitch motion control commands and the flight parameters are used as inputs.
An adaptive neural network model for vibration control in a Blackhawk helicopter
Adaptives neuronales Netz zur Schwingungsbegrenzung in einem Blackhawk-Helikopter
Journal of the American Helicopter Society ; 50 , 4 ; 349-353
2005
5 Seiten, 3 Bilder, 8 Quellen
Aufsatz (Zeitschrift)
Englisch
An Adaptive Neural Network Model for Vibration Control in a Blackhawk Helicopter
Online Contents | 2005
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