Autonomous racing has emerged as a safe and scalable environment to accelerate research on high-performance motion planning and control. This thesis addresses the problem of trajectory tracking for autonomous racing vehicles, focusing on the development of advanced control strategies for the F1Tenth platform. While geometric controllers such as Pure Pursuit or Clothoid-based methods are widely used for realtime path tracking due to their simplicity and robustness, they struggle to capture complex vehicle dynamics and nonlinear tire behavior. Model-based approaches like MPC can account for these effects but often rely on simplified models and entail high computational costs, limiting their real-time performance and adaptability under varying conditions.