Optimizing Motor Performance with Improved Fractional Order Darwinian Particle Swarm Optimization and Fuzzy Logic Controllers: A Comparative Study in Torque Control

Utilizing fuzzy logic controllers (FLCs) and the improved fractional ordered Darwinian particle swarm optimization method (IFODPSO), this paper demonstrates a way to boost motor performance. The system is able to generate torque instantly and respond quickly because it uses direct torque control (DTC) approaches that are regulated by IFODPSO. Motor performance improvement with IFODPSO and fuzzy logic controllers is the motive of this study. The regulation of torque in motor applications is the particular issue that is being tackled. The research compares the IFODPSO-FLC method's performance with that of typical field-oriented control (FOC) method and DTC method. In contrasted with more conventional FOC and DTC methods, the results obtained by the IFODPSO-FLC methodology show promise for torque control, highlighting the significance of the findings. To further enhance system efficiency at low speeds, the suggested PI-fuzzy opposition estimation accounts for fluctuations in stator resistance. An innovative and effective strategy is the integration of fractional-order FLCs with Darwinian particle swarm optimization (DPSO). The outcomes are assessed with the use of MATLAB-Simulink and the performance that is derived from them shows promise for effective motor control applications.