To address the issues of insufficient control accuracy, difficult parameter tuning, and dynamic response lag caused by nonlinearity and multi-parameter time-varying characteristics in PMSM (permanent magnet synchronous motor) control systems, CPFIBA (chaotic potential field improved bat algorithm) based on the artificial potential field method was proposed for optimizing fuzzy PID control system parameters. This algorithm integrates the core concepts of the chaotic artificial potential field method and PSO (particle swarm optimization) algorithm. By introducing a dynamic learning factor, it significantly enhances global search capability and environmental adaptability while accelerating algorithm convergence, thereby effectively improving the control accuracy of permanent magnet synchronous motors. Experimental validation demonstrates that the proposed CPFIBA-fuzzy PID control system achieves a step response time of 0.09 s under high-speed operation and a disturbance settling time of 0.062 4 s under low-speed load conditions. This control system exhibits superior response speed and disturbance rejection capability, effectively enhancing the motor’s adaptability and reliability across diverse operating conditions.