Robust speed-controlled induction motor drive based on recurrent neural network

This paper proposes a recurrent neural network speed controller for an induction motor drive. This speed controller consists of a recurrent neural network identifier (RNNI) and recurrent neural network controller (RNNC). The RNNI is used to provide real-time adaptive identification of the unknown motor dynamics. The RNNC is used to produce an adaptive control force so that the motor speed can accurately track the reference command. A back-propagation algorithm was used as the learning algorithm to automatically adjust the weights of the RNNI and RNNC in order to minimize the performance functions. The proposed control scheme can quickly estimate the plant parameters and produce a control force, such that the motor speed can accurately track the reference command. Both computer simulations and experimental results demonstrated that the proposed control scheme was able to obtain robust speed control.