Lateral and longitudinal control of an autonomous racing vehicle.

The development of autonomous and intelligent vehicles is increasing continuously in the aim to reach a reliable and secured transportation system. Indeed, autonomous navigation include three main steps: perception and localization, planning and control. This thesis covers essentially the study of the vehicle modeling and the vehicle control, focused on the coupled lateral and longitudinal control of the autonomous racing vehicle. Three different control strategies are considered: First one based on coupled control while the second one is decoupled control. In coupled controller, adaptive model predictive control (MPC) is used which handles both lateral and longitudinal control. In the decoupled control strategy, longitudinal dynamics is controlled with the help of a PID and Lateral dynamics is controlled first with MPC and second with Lateral controller. The proposed strategy utilizes an adaptive MPC to perform lateral guidance and speed regulation by acting on the front wheel steering angle and acceleration/deceleration to minimize the vehicle’s lateral deviation and relative yaw angle with respect to the reference trajectory, while driving the vehicle within the limits of adherence conditions. While designing the Adaptive-MPC, the internal plant model for MPC is modeled using a linear bicycle model, while dynamics of the vehicle is modeled using a 3 degree of freedom dual-track rigid vehicle model considering the non-linear tire forces derived from a Pacejka model taking into account the slip ratio. The objective is to develop and analyse the three different control strategies and evaluate their design and performance through path following, speed tracking, and ease of implementation. The overall system has been developed using MATLAB® and Simulink®.