Abstract
In the last decades autonomous vehicles have been at the centre of the research in both the academic and the industrial fields, but not without difficulties. In particular, the problem of path planning and tracking at the limit of the handling capabilities of a vehicle poses many challenges from a control perspective, and it is yet to be understood whether the integration with stability controllers can improve the cornering performance of autonomous vehicles as much as it does for human drivers. This thesis aims to provide insights on these topics. The first part of the work is dedicated to the planning and tracking layers of an autonomous vehicle driving on racetracks. The analysis covers the offline optimisation of the trajectory and the description of a re-planning algorithm for the avoidance of obstacles. A comparison among several path tracking controllers is then provided, to understand whether the gain in performance obtained from advanced controllers justifies the design complexity. In the second part, the thesis highlights the benefits of yaw rate control on the behaviour of over-actuated vehicles. An algorithm for yaw rate control is introduced and implemented in a torque vectoring controller, and the proof of asymptotic stability of the system is provided. Several application examples are presented, with simulation and experimental results that demonstrate the potential and versatility of yaw rate control. Finally, the integration of torque vectoring and path tracking control in an autonomous racing vehicle is presented and assessed with a simulation study along obstacle avoidance tests. The results of the thesis show that: i) including road preview information in path tracking controllers improves the control action, resulting in better vehicle behaviour, and ii) torque vectoring control always improves the vehicle performance, and it also enhances the system robustness to variations in the tyre-road friction coefficient.