This paper discusses the application of recurrent neural networks for identification and control of helicopter vibrations. A class of re-current networks called Memory Neuron Networks are used for plant identification and control. These networks are obtained by adding train-able temporal elements to feed-forward networks. This makes the net-work output history sensitive and gives them the capability to identify and control systems whose order is unknown or systems with unknown delay. A representative analytical model with higher harmonic pitch angles for minimizing hub shear forces is used for simulation. The ef-fectiveness of the controller in minimizing the force level at varying and constant forward speed are studied. The ability of the controller to cope with changes in system and environment parameters is also considered. KEY WORDS: neural networks, adaptive controllers, helicopters 1
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