Artificial neural networks in vibration control of rotor-bearing systems

Abstract

A neural network controller is described and implemented for controlling the vibration of a rotor-bearing system. A multi-layered neural network is used to model the inverse dynamics or the rotor-bearing system on-line. It is learnt by a backpropagation algorithm, and a 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.