Trajectory Tracking Control of an Autonomous Vehicle using Model Predictive Control and PID Controller

Over the years, there has been a substantial increase in the number of vehicular traffic, which has led to vital problems like car crashes and congestion. More than 90 percent of collisions are the result of human error. Technology that allows for autonomous driving has the potential to enhance traffic efficiency and safety. Based on knowledge about the nearby traffic, an autonomous vehicle can create a trajectory and follow it using control algorithms. A significant technology in the study and implementation of autonomous vehicles is trajectory tracking control. Paths are a series of instructions that provide directional directives to get to a specific location, whereas a trajectory includes the schedule of velocity and higher order words, such as acceleration in terms of the body’s longitudinal and lateral motion, that are necessary to reach there. In this study, PID controllers and model predictive controllers (MPC) are used to govern the trajectory of an autonomous vehicle. The performance of the autonomous vehicle using both the controllers are then compared. The work is validated using simulations on MATLAB simulink.