Implementing Nonlinear Model Predictive Control for Enhanced Trajectory Tracking and Road Anomaly Avoidance in Autonomous Vehicles

In the rapidly evolving realm of autonomous vehicles, trajectory tracking stands as a cornerstone for ensuring safety and precision in navigation. Traditional linear methodologies, although efficient to some extent, can be inadequate in addressing the challenges posed by complex urban environments and defective roads. Nonlinear Model Predictive Control (NMPC), with its capability to anticipate future events and adapt accordingly, presents an opportunity to redefine the trajectory tracking landscape. Therefore, this study presents the integration of NMPC for trajectory tracking in autonomous vehicles. Through a comprehensive analysis and simulations, this study showcases improved tracking accuracy, and enhanced adaptability in diverse scenarios. Our findings underscore the potential of NMPC in setting a new benchmark for trajectory tracking, ultimately pushing the boundaries of what autonomous vehicles can achieve.