Developed an Improved Fractional Ordered Darwinian Particle Swarm Optimization Using Fuzzy Logic Controller to Improve the Performance Measures on E-Vehicle

Abstract

This study introduces the improved fractional ordered Darwinian particle swarm optimization (IFODPSO) method combined with fuzzy logic controllers to optimize motor performance. By utilizing direct torque control (DTC) techniques controlled by IFODPSO, the system achieves quick response times and instantaneous torque generation. The research addresses the optimization of motor performance using the IFODPSO approach for combination with fuzzy logic controllers. The specific problem being addressed is the control of torque in motor applications. The scope of the research includes comparing the performance of the IFODPSO-FLC approach with common field-oriented control (FOC) as well as DTC approaches. The significance of the results attained is noteworthy as the IFODPSO-FLC approach demonstrates promising outcomes in terms of torque control compared to traditional FOC and DTC techniques. Additionally, the proposed PI-fuzzy opposition estimation helps improve system efficiency at low speeds by compensating for variations in stator resistance. The combination of fuzzy logic controllers and Darwinian particle swarm optimization of fractional order approaches presents a novel and efficient solution. The results are evaluated using MATLAB-Simulink, and the obtained performance demonstrates the potential for successful motor control applications.