From: 2025-08-18 11:00 to 12:00
Place: Seminar Room M 3170-73 in the M-building, LTH
Contact: bjorn [dot] olofsson [at] control [dot] lth [dot] se

Date & Time: August 18th, 11:00-12:00
Location: Seminar Room M 3170-73 in the M-building, LTH
Author: Leonid Meledin
Title: Comparing Gradient-Based and Sampling-Based Model Predictive Control for Autonomous Racing
Supervisor:  Yiannis Karayiannidis (LTH) 
Examiner:  Björn Olofsson (LTH)

Abstract: This work 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-based approach known as model predictive path integral (MPPI) control. The controllers utilize longitudinally and laterally coupled non-linear kinematic and dynamic bicycle models, and transition between models is achieved through speed-based linear blending of the state update functions. High-fidelity system identification of the vehicle dynamics is achieved using a physics-constrained neural network (PCNN), with the learned dynamics employed in simulation for controller tuning. The final evaluation consists of a head-to-head comparison, where the vehicle, driven by each controller in turn, completes a ten-lap time attack on a scaled-down Formula Student Germany 2023 Driverless Cup track. We conclude that MPC outperforms MPPI in terms of lap times and tracking performance, while MPPI exhibits superior consistency in computational time.