Genetic Algorithm Optimized Adaptive-Fuzzy Fractional Order PID Speed Control of Permanent Magnetic Synchronous Motor for Electric Vehicle Application

Abstract

This thesis concentrated on the genetic algorithm tuned adaptive fuzzy fractional order proportional integral derivative speed control of permanent magnet synchronous motor (PMSM) drive for electric vehicle (EV) application.EVs offer a different approach to addressing transportation-related energy consumption and efficiency problems. Due to its many benefits, including high efficiency, compact volume, light weight, high reliability, low maintenance requirements, good control characteristics, and attraction to the EV industry, PMSM is being used in EV drive systems more and more. This kind of motor drive is one of the greatest options for applications requiring a wide range of motion control. It is frequently utilized in machine tools and robotics, and it is also being explored for high-power applications like industrial drives and vehicle propulsion. It also contains traits like non-linearity, time variation dynamics, uncertainty, and accessible states and outputs that make control over them extremely challenging. The parameter of the fractional order proportional integral derivative (FOPID) controller is realized by the genetic algorithm, which has good adaptability to parameter changes, non-linearity, and an imperfect model of the controlled object. The simulation results showed that, in comparison to PID, fractional order PID controllers and adaptive fuzzy fractional order PID controllers, the designed genetic algorithm optimized adaptive fuzzy fractional order proportional integral derivative (GA-AFFOPID) controller realized a good dynamic behavior of the system, perfect speed tracking, and ensured robustness against parameter variations and suddenly load disturbance while maintaining good dynamic performance. Finaly Compared to the standard adaptive fuzzy fractional order pid the result show that genetic algorithm optimized adaptive fuzzy fractional order PID exhibits superior performance with 1.796% lower overshoot,0.97% faster rise time,4.25% lower steady state error,and 0.35% faster settling time than the adaptive fuzzy fractional order PID controller.These finding suggest that the genetic algorithm optimization technique can significantly enhance tthr control performance of the adaptive fuzzy fractional order PID controller.