A multi-criteria benchmarking framework for direct torque control strategies in induction motor drives

This paper introduces a systematic framework for the performance evaluation of various direct torque control (DTC) techniques for induction motor drives. Although the conventional direct torque control (CDTC) method provides a rapid response to the changes, it is associated with considerable torque ripple and variable switching frequencies. The introduction of ANN-based DTC control in recent years has gained considerable attention due to its non-linear learning ability and dynamic response characteristics. Nevertheless, the comparative assessment of DTC control is difficult without an efficient framework for evaluating their performance. The purpose of this paper is to systematically analyze the benchmarking process through literature review and statistical analysis. With the goal of bridging the current gap in knowledge, this paper develops a new approach for evaluating the performance of DTC control methods. The proposed framework evaluates the DTC techniques on the basis of the following parameters: rise time, settling time, percentage of overshoot, peak time, and torque ripple. The proposed framework can be effectively applied for the objective evaluation of various DTC techniques, including conventional direct torque control, ANN-DTC, Fuzzy-DTC, FOC, SMC-DTC, and SVPWM-DTC. The effectiveness of the proposed framework can be validated with the simulation results, which reveal that the proposed ANN-DTC method provides the highest OPI value of 0.92. The robustness of the proposed framework can be validated with the application of the proposed framework and the application of the multi-criteria decision-making method.