Performance Evaluation of Linear Model-Based Predictive Control for Path Tracking in Light Autonomous Vehicles

This article focuses on the control of autonomous vehicles (AVs) using advanced methodologies, with particular emphasis on Model-based Predictive Control (MPC) as a tool for optimizing trajectory replication. The primary objective is to demonstrate that MPC can effectively minimize costs and improve efficiency in urban traffic scenarios. The study explores control strategies centered on reducing energy consumption and response time. Given the extensive research on this topic, the article evaluates and compares various control methods, including Pole Allocation, Linear Quadratic Regulator (LQR), and MPC, highlighting the superior capabilities of MPC in ensuring stability and adaptability. Simulations conducted in MATLAB are utilized to validate these approaches, focusing on maintaining trajectory stability during variations in the steering angle.