Permanent Magnet Synchronous Motor (PMSM), owing to their high energy efficiency and structural advantages, has been widely adopted in high-performance motion control applications such as rail transit systems. However, chaotic behaviors inherent in PMSM systems can lead to operational instability, necessitating highly effective control strategies. This paper proposes a dual-parameter cooperative control strategy—referred to as GWO-Volterra—based on the integration of Grey Wolf Optimization (GWO) and the Volterra series, aiming to achieve precise control of chaotic motions in PMSMs. In this strategy, the distance between two adjacent projection points on the Poincaré section is selected as the control input. Furthermore, the complex coupling effects of system parameters on dynamic behavior are thoroughly considered by constructing a dual-parameter controller within the Volterra series framework. To enhance control performance, the GWO algorithm is introduced to optimize and adaptively adjust key parameters. Simulation results demonstrate that, compared with single-parameter control strategies, the proposed GWO-Volterra approach exhibits superior performance in terms of faster response, reduced overshoot, and enhanced control stability, thereby validating its effectiveness and practical applicability.