Design And Validation Of Model Predictive Controllers For Lateral Vehicle Control

This thesis develops, implements, and evaluates a Model Predictive Control (MPC) framework for lateral vehicle guidance using a linearized bicycle model. The complete control architecture is formulated from first principles, beginning with the derivation of the continuous-time planar dynamics and their conversion into a discrete-time state-space representation suitable for real-time control. The MPC problem is constructed explicitly through the development of prediction models, output selection matrices, and quadratic cost formulations. The controller is evaluated through a Python simulation environment that includes reference trajectory generation, nonlinear vehicle propagation, and constraint handling. Robustness is examined using systematic perturbations to vehicle mass, tire cornering stiffness, yaw inertia, and forward velocity. A Monte Carlo analysis with randomized parameter variations quantifies statistical tracking performance under uncertainty. Simulation results demonstrate that MPC achieves smooth steering behavior and improved lateral tracking accuracy, especially under parameter mismatch. Monte Carlo results show that MPC maintains low variance and strong robustness across the tested uncertainty ranges. Overall, the presented framework provides a transparent, reproducible, and computationally efficient control architecture suitable for autonomous ground vehicles, academic research, and controller rapid prototyping.