Finite control set predictive torque control for induction motor drive with simplified weighting factor selection using TOPSIS method

Abstract

Finite control set predictive torque control (FCS-PTC) becomes popular for induction motor drives due to its simple structure and flexibility of including additional control parameters into the control law. However, primary concern of this control technique is the selection of suitable weighting factors in the cost-function. Usually, empirical method is used to select the weighting factors, which is time-consuming and heuristic process. In this study, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is introduced in the cost-function optimization to simplify the difficulties involved in the weighting factor selection. This method selects an optimal control action, which is closer to positive ideal control action and far away from negative ideal control action. This ensures the selection of optimal control action in each sampling period based on the priorities given to control parameters in the cost-function. Further, to reduce the computational burden of proposed technique, a predefined set of switching states are used for the cost-function optimization based on previous optimal control action. Both simulation and experimental studies are carried out for a two-level voltage source inverter fed induction motor drive. These results are compared with conventional FCS-PTC technique to highlight the merits of proposed technique.