Recurrent-neural-network-based implementation of a programmable cascaded low-pass filter used in stator flux synthesis of vector-controlled induction motor drive

Abstract

The concept of programmable cascaded low-pass filter for stator flux vector synthesis by ideal integration of stator voltages at any frequency was introduced by Bose and Patel. A new form of implementation of this filter is proposed that uses a combination of recurrent neural network trained by Kalman filter and a polynomial neural network. The proposed structure is simple, permits faster implementation by digital signal processor, and gives improved performance.