To address the pose deviation issues faced by Wheeled Mobile Robots （WMRs） during trajectory tracking due to inaccurate positioning and unknown disturbances， an Enhanced Particle Swarm Optimization Mixer （EPSO-Mixer） algorithm based on Geometric Model Predictive Control （GMPC） was proposed， aiming to enhance trajectory tracking performance of WMRs. Firstly， an Enhanced Particle Swarm Optimization （EPSO） algorithm was proposed on the basis of Particle Swarm Optimization （PSO） to accelerate convergence and improve optimization capabilities. Secondly， EPSO was used to improve GMPC by selecting optimal tracking parameters according to the current deviation level， so as to reduce trajectory tracking errors effectively. Finally， by integrating the Multi-Layer Perceptron Mixer （MLP-Mixer） architecture， EPSO-Mixer algorithm was proposed， thereby further enhancing search capability for the global optimum and generating more adaptive control strategies. Simulation results show that compared with nonlinear model predictive control and classic GMPC algorithms， EPSO-Mixer GMPC improves trajectory tracking performance of WMRs under pose deviation conditions effectively with errors reduced by 8.0% - 82.3% and mitigating vibration issues during motion significantly. These results indicate that EPSO-Mixer algorithm provides more effective control strategies， thereby reducing the complexity and time cost of parameter adjustment， and enhancing the adaptability of trajectory tracking control significantly.