Modeling of genetic algorithm tuned adaptive fuzzy fractional order PID speed control of permanent magnet synchronous motor for electric vehicle

Abstract

Abstract This study presents a novel Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative (GA-AFFFOPID) controller for enhancing the speed control performance of permanent magnet synchronous motor (PMSM) drives in Electric Vehicles. The proposed GA-AFFFOPID controller, which combines the advantages of genetic algorithm optimization and adaptive fuzzy fractional-order PID control, represents a unique and innovative approach to address the control challenges associated with PMSM drives. Permanent magnet synchronous motor technology, known for its efficiency, compactness, reliability, and versatility in motion control applications, is increasingly adopted in electric vehicle drive systems. However, the inherent non-linearity, dynamics, and uncertainties of permanent magnet synchronous motors pose significant control challenges. The exceptional performance of the GA-AFFFOPID controller, demonstrated through its superior system dynamics, precise speed tracking, and robustness against parameter variations and sudden load disturbances, underscores the significant advancements enabled by the genetic algorithm optimization technique in improving the control performance of PMSM drives for electric vehicle applications. Comparative analysis with traditional control methods demonstrates the exceptional performance of the Genetic Algorithm-optimized Adaptive Fuzzy Fractional Order Proportional Integral Derivative controller. These findings 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.

Keywords

• Electric vehicle
• Adaptive fuzzy
• Permanent magnet synchronous motor