Seyed Alireza Davari

PhD Thesis Defense Session

Abstract:
The predictive torque control (PTC) method is being implemented by means of speed sensor in most cases. Also, in model predictive control, adjusting the weighting factor is an important challenge. Therefore, the PTC method has not succeeded to pave its way to the industrial applications.
The contributions of this dissertation are categorized to two main parts. In the first part, two novel methods for weighting factor calculation are developed. In the first method, weighting factor of the cost function is calculated via an optimization method in order to minimize the torque ripple. In the second method, a look-up table base method for two-step prediction method is developed. The second part of the dissertation is dedicated to the proposed sensorless predictive torque control methods. Finite control set model predictive control (FCS-MPC) method and dead-beat method are combined with full order and voltage model observers. In order to reduce the effect of sensorless estimation on sensorless prediction, a robust prediction model is proposed for FCS-MPC and an inherently sensorless prediction model is proposed for dead-beat control. For precise estimation of the states, robust full order observer and robust reduced order observer are developed. The robustness of the prediction model and observers is achieved by H-infinity analysis.
In order validate the proposed methods, simulation and experimental results are presented and analyzed. Low speed performance, robustness against the variation of the stator and rotor resistances and robustness against current measurement offset are examined to select the most useful method.

Date: 2012/102/26 Sunday
Location: Room 304 Electrical Engineering Faculty