Vibration control of rotor-bearing systems using neural networks

A neural network controller is described and implemented for controlling the vibrations of a Rotor Bearing System. A multi-layered neural network is used to model the inverse dynamics of the rotor-bearing system on-line; it is learned by backpropagation algorithm, and delta rule in which the difference between the actual control input to the plant, which is generated from the neural controller, and the input estimated from the inverse-dynamics model by using an actual plant output is minimized. The results show a satisfactory diminished response of the rotor-bearing system when the controller is applied to the system.