Application of neural network in suppressing mechanical vibration of a permanent magnet linear motor

In this study, a recurrent neural network compensator for suppressing mechanical vibration in a permanent magnet linear synchronous motor (PMLSM) is studied. The linear motor is controlled by a conventional PI velocity controller, and the vibration of the flexible mechanism is suppressed by using a hybrid recurrent neural network. The differential evolution strategy and Kalman filter method are used to avoid the local minimum problem, and estimate the states of system, respectively. The proposed control method is firstly designed by using a nonlinear simulation model built in Matlab Simulink and then implemented in a practical test rig. The proposed method works satisfactorily and suppresses the vibration successfully.