Multi-phase motors have recently replaced three-phase induction motors in a variety of applications due to the numerous benefits they provide, and the absence of speed sensors promotes induction motors with variable speed drives. Sensorless speed control minimizes unnecessary speed encoder cost, reduces maintenance, and improves the motor drive’s reliability. The performance comparison of the fuzzy sliding mode controller (FSMC) with adaptive fuzzy proportional integral derivative (AFPID) control methods for sensorless speed control of six-phase induction motors was analyzed in this study, and the proposed control system has an advantage for multiphase machines, specifically six-phase induction motors (IMs) in this study, as they are the current active research area for electric vehicles, hybrid electric vehicles, aerospace, ship propulsion, and high-power applications. The speed control of a six-phase induction motor was performed by using an AFPID controller and FSMC. The comparative performance analysis was based on sensorless speed control of the six-phase induction motor. A proportional integral derivative (PID) controller is commonly employed as it is used to eliminate oscillations, but it has several drawbacks, such as taking a long time to decrease the error and stabilize the system at constant speed. The fuzzy type-2 and PID controllers were hybridized so as to obtain the advantages of both to enhance the system performance. Finally, the comparison result revealed that the FSMC preforms significantly better by achieving good tracking performance. The control technique maintains the sliding mode approach’s robustness while providing reduced overshoots with a smooth control action, and the FSMC revealed good dynamic response under load variations when compared to the AFPID controller.
Recent research reveals that multi-phase motors in electric propulsion systems are highly recommended due to their improved reliability and efficiency over traditional three phase motors. This research presented a comparison of optimal model design of a six phase squirrel cage induction motor (IM) for electric propulsion by using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). A six phase squirrel cage induction motor is designed and simulated by ANSYS Motor-CAD. In order to find the best fit method, simulation results are compared and applied to the motors for electric propulsion, considering the influence of design upon the motor performance. The six-phase squirrel cage induction motor is more energy efficient, reliable and cost effective for the electric propulsion compared to the conventional three phase motor. In this study, first the initial parameters of the six phase squirrel cage induction motor have been determined and then these parameters have been compared with optimized values by Genetic Algorithm (GA) and PSO optimization. The motor designed is optimized using efficiency and power losses as the fitness function. The six phase squirrel cage induction motor is designed using ANSYS Motor-CAD and the simulation results were also presented along with two-dimensional and three-dimensional geometry. The result shows that the weight and power loss are reduced to 161 kg and 0.9359 Kw respectively, while the efficiency and power factor are increased to 0.95 and 0.87 respectively when PSO is used. This shows that the result is promising.
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