Model Predictive Control for pathtracking and obstacle avoidance of autonomous vehicle

Abstract: The concept of autonomous vehicles has been widelyexplored lately by, among others, automotive companies as away to for example improve fuel efficiency or to gain accessto environments which pose a danger to human operators.Model Predictive Control (MPC) has traditionally been used tocontrol systems with slower dynamics but with the emergence ofmore powerful computers it is now being used in systems withconsiderably faster dynamics as well. One of the main strengthsof MPC is its ability to handle constraints which are present inall physical systems. The aim of this thesis was to develop a singlelayer linear controller for path tracking and obstacle avoidanceof an autonomous car. Its ability to minimize the deviations tothe reference path while clearing static obstacles was evaluated.Focus was placed on the tracking problem hence no trajectoryplanning system was implemented. Instead a predefined pathwas used. Simulations were developed in MATLAB based on thekinematic bicycle model. The performance of the controller wasfurther tested at Smart Mobility Lab (SML) in KTH where amodified R/C car was controlled through Robotics OperatingSystem (ROS). The results from the experiments showed that itwas able to successfully evade the obstacles while tracking thepath. However, in the experiments the vehicle failed to respectthe requirements on maximum deviation from the obstacles andthe path.