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.
