Torque ripple reduction of Induction Motor using Dynamic Fuzzy Prediction Direct Torque Control

Area of Science:

• Electrical Engineering
• Control Systems
• Motor Drives

Background:

• Squirrel cage induction motors (SCIMs) are widely used in industrial applications due to their robustness and self-starting capabilities.
• Torque ripple is a significant issue in SCIMs, affecting performance and efficiency, especially during transient conditions.
• Existing control methods often struggle to effectively minimize torque ripples across a wide range of operating speeds.

Purpose of the Study:

• To propose and evaluate a novel Dynamic Fuzzy Logic based Prediction Direct Torque Control (DFL-PDTC) technique for SCIMs.
• To significantly reduce torque ripples and improve the dynamic performance of SCIMs.
• To optimize the control parameters using a metaheuristic algorithm for enhanced ripple reduction.

Main Methods:

• Implementation of a Dynamic Fuzzy Logic Controller (DFLC) with Gaussian Membership Functions (GMFs) for speed control.
• Integration of Prediction Direct Torque Control (PDTC) to select optimal voltage vectors based on torque and flux errors.
• Optimization of FLC parameters (α, c) using the Flower Pollination Algorithm (FPA).

Main Results:

• The proposed DFL-PDTC technique effectively reduces torque ripples in SCIMs.
• Improved starting conditions and motor performance were observed at both low and high mechanical speeds.
• Experimental validation in MATLAB/Simulink confirmed the efficiency of the FPA-optimized DFL-PDTC approach.

Conclusions:

• The DFL-PDTC technique, optimized with FPA, offers a superior solution for minimizing torque ripples in SCIMs.
• The method enhances motor control accuracy and efficiency, making it suitable for demanding industrial applications.
• The study demonstrates the effectiveness of combining fuzzy logic, predictive control, and metaheuristic optimization for advanced motor drive systems.