Advanced Direct Torque Control: Employing Fuzzy Logic for Dynamic and Adaptive Regulation

Abstract

this article presents a novel approach to Direct Torque Control (DTC) for doubly fed induction machines using fuzzy logic. Traditional DTC suffers from limitations due to its inflexible switching strategy, treating large and small torque and flux errors identically. This can lead to suboptimal performance, particularly during start up or when reference values fluctuate. Our proposed solution leverages a streamlined fuzzy logic controller with a minimized rule set to enhance the switching strategy. This approach replaces the conventional hysteresis-based regulators and switching table, resulting in reduced computational burden and a faster sampling period. This improved sampling rate translates to significantly smoother torque and flux control with reduced ripple. Furthermore, we've integrated fuzzy logic as a supervisory element to dynamically adjust the PI controller gains for speed regulation, effectively creating a nonlinear PI controller with adaptive parameters. Simulations conducted in MATLAB/SIMULINK showcase the effectiveness and superior performance of this advanced DTC strategy.