A Robust Fuzzy-PI Controller for Doubly Fed Induction Machines Using 12 Linguistic Variables

Abstract

This study focuses on the formulation of a robust nonlinear control strategy for the Doubly-Fed Induction Machine (DFIM), with the objective of improving its performance in variable-speed drive applications. This approach combines vector control with intelligent techniques, specifically Type-1 Fuzzy Logic, utilizing 12 linguistic variables, to address the shortcomings of traditional control methods. Conventional vector control frequently experiences challenges, including oscillations, sluggish response, and diminished efficiency across different load and speed scenarios. The introduction of fuzzy logic enhances the adaptability and robustness of the control system, thereby significantly improving its dynamic response and steady-state performance. We conducted an extensive simulation to evaluate the effectiveness of the proposed fuzzy-vector control strategy. The results demonstrate that the integration of fuzzy logic reduces oscillations and improves load-driving capability across a range of speeds. Additionally, it enhances response time, resolves derivation issues, and minimizes flux ripples and fluctuations in electromagnetic torque. Overall, the findings suggest that fuzzy logic surpasses traditional vector control, offering a more reliable and efficient solution for modern DFIM applications.