Available at: https://digitalcommons.calpoly.edu/theses/3279
Date of Award
6-2026
Degree Name
MS in Mechanical Engineering
Department/Program
Mechanical Engineering
College
College of Engineering
Advisor
Charlene Birdsong
Advisor Department
Mechanical Engineering
Advisor College
College of Engineering
Abstract
This thesis compares a model predictive controller (MPC) and a lateral Stanley controller for vehicle path-tracking applications under simulation-based and perception-driven operating conditions. Both controllers were evaluated in simulation using a nonlinear dynamic bicycle model executing single and double lane change maneuvers. Following simulation-based evaluation, both controllers were implemented on hardware within a perception-driven steering-control pipeline. This pipeline utilized recorded sensor data from the MXcarkit 1/8th-scale autonomous vehicle platform, incorporating lane instance segmentation and homography-based roadway estimation.
Under idealized simulation conditions, the MPC demonstrated improved trajectory-tracking performance during aggressive maneuvers while requiring greater steering activity and computational effort relative to the Stanley controller. During perception-driven replay evaluation, both controllers demonstrated highly similar agreement with time-synchronized steering commands recorded during manual vehicle operation despite their distinct control philosophies. These results suggest sensing uncertainty and roadway estimation may limit observable differences between controller architectures, potentially reducing the practical advantage of MPC and motivating improved localization to realize its performance benefits in real-world operation.
Included in
Acoustics, Dynamics, and Controls Commons, Controls and Control Theory Commons, Electro-Mechanical Systems Commons, Navigation, Guidance, Control, and Dynamics Commons