Comparing Gradient-Based and Sampling-Based Model Predictive Control for Autonomous Racing
This thesis investigates the performance of two model predictive control (MPC) variants for autonomous racing, using a 1:10-scale vehicle as a testbed for reference path-tracking experiments. The platform is equipped with a single inertial navigation system (INS) sensor, providing highly accurate odometry measurements. The controllers under study are the standard gradient-based MPC and a sampling-
