Modeling of Genetic Algorithm Tuned Adaptive Fuzzy Fractional Order PID Speed Control of PMSM for Electric Vehicle

Abstract

Abstract This study presents a novel approach to enhance the speed control performance of Permanent Magnet Synchronous Motor (PMSM) drives in Electric Vehicles (EVs) through the implementation of a Genetic Algorithm (GA)-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFOPID) controller. PMSM technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in EV drive systems. However, the inherent non-linearity, dynamics, and uncertainties of PMSMs pose significant control challenges. The proposed GA-AFFOPID controller, tuned using a genetic algorithm, exhibits superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances. Comparative analysis with traditional control methods demonstrates the exceptional performance of the GA-AFFOPID controller, achieving a 1.796% lower overshoot, 0.97% faster rise time, 4.25% lower steady-state error, and 0.35% faster settling time compared to the adaptive fuzzy fractional order PID controller. These results highlight the significant performance improvements facilitated by the genetic algorithm optimization technique in enhancing the control performance of the adaptive fuzzy fractional order PID controller in PMSM drives for electric vehicle applications, paving the way for improved energy efficiency and overall performance of electric vehicle propulsion systems.