Advanced VSC and intelligent control algorithms applied to SVM_DTC for induction motor drive: A comparative study

Abstract

Application of intelligent control methods offers the best solutions to Adjustable Speed Drives (ASD's). Automation industry demands various types of load profiles to be tracked by the drives. Conventional control algorithms, though simple, have their limitations and fail in offering the required responses. Intelligent control algorithms can very well cope-up with the practical situations like nonlinearities, uncertainties and disturbances. The intelligent decision system plans and controls the drive system. Further, the computational power of processors is no longer a limitation. This paper presents a performance comparison between three intelligent control algorithms for 3-phase, VSI fed squirrel cage Induction Motor (IM). The IM drive has Space Vector Modulated Direct Torque Control (SVM_DTC); controlled by three different approaches: 1. Artificial Intelligent (AI) Fuzzy based technique for PI tuning, and 2. Hybrid Fuzzy_sliding mode control theory 3. Artificial Neural Networks (ANN). All methods are applied to SVM_DTC, and each control strategy is tested for its robustness to parameter changes and, disturbance rejection capabilities. A model of SVM_DTC drive is developed on MATLAB-SIMULINK environment to examine the performance of intelligent and modern control algorithms. A performance index based on speed error is selected as a parameter for comparison of these three strategies.