Nam-Hun Kim

Position sensorless control system of SRM using neural network

This paper presents a position sensorless control system of switched reluctance motor (SRM) using neural network. The control of an SRM depends on the commutation of the stator phases in synchronism with the rotor position. The position sensing requirement increases the overall cost and complexity. In this paper, the current-flux-rotor position look-up table based position sensorless operation of a SRM is presented. Neural network is used to construct the current-flux -rotor position lookup table, and is trained by sufficient experimental data. Experimental results for a 1-hp SRM is presented for the verification of the proposed sensorless algorithm.

A position sensorless control system of SRM over wide speed range

This paper presents a position sensorless control system of SRM over wide speed range. The rotor position estimation of the SRM is somewhat difficult because of its highly nonlinear characteristics. In this paper, the magnetizing curve of prototype 1-hp SRM is obtained from locked rotor test and complicated data patters are trained using neural network. To estimate more accurate rotor position, current-flux-rotor position lookup table over wide operating rage is generated using trained neural network. Experimental result for a 1-hp SRM over 16:1 speed control range is presented for the verification of the proposed sensorless control algorithm.

An optimal efficiency control of reluctance synchronous motor using neural network with direct torque control

This paper presents an implementation of efficiency optimization of reluctance synchronous motor (RSM) using a neural network (NN) with an direct torque control (DTC). The equipment circuit in RSM, which consider with iron losses is theoretically analyzed and the optimal current ration between torque current and exiting current analytically derived. For RSM, torque dynamics can be maintained even with controlling the flux level because a torque is directly proportional to the stator current unlike induction motor. In order to drive RSM at maximum efficiency and good dynamics response, the NN is used. The experimental results are presented to validate the applicability of the proposed method. The developed control system show high efficiency and good dynamic response features with 1.0 [kW] RSM having 2.57 ratio of d/q.