TY - JOUR
T1 - A Study of Model Predictive Control for Autonomous Vehicle Path Tracking Control Regarding Passenger Safety Using MATLAB and ROS
AU - Widjiantoro, Bambang Lelono
AU - Indriawati, Katherin
AU - Arfianti, Rida Ayu
AU - Wahyuadnyana, Kadek Dwi
N1 - Publisher Copyright:
© 2024 Praise Worthy Prize S.r.l.-All rights reserved.
PY - 2024
Y1 - 2024
N2 - Autonomous Vehicles, or AV technology, is rapidly progressing today. AVs enable vehicles to operate autonomously, eliminating the need for human involvement. During autonomous operation, AVs must be able to sense their surroundings and autonomously adjust speed and direction to ensure passenger comfort and safety. Hence, a reliable control system is essential for these tasks. MPC is a commonly used control algorithm for AVs, generating optimal adjustments based on predefined constraints. While many studies have explored MPC's effectiveness in AVs, most have been limited to simulations. Therefore, this study investigates the application of MPC in AVs through simulation and real-world testing. Alongside MPC, several other control algorithms, such as PID and Stanley, were also evaluated to compare their performance, considering the vehicle's kinematic and dynamic properties. Results from simulations and experiments indicate that MPC achieves precise trajectory tracking and facilitates smooth changes in direction, enhancing passenger comfort and safety.
AB - Autonomous Vehicles, or AV technology, is rapidly progressing today. AVs enable vehicles to operate autonomously, eliminating the need for human involvement. During autonomous operation, AVs must be able to sense their surroundings and autonomously adjust speed and direction to ensure passenger comfort and safety. Hence, a reliable control system is essential for these tasks. MPC is a commonly used control algorithm for AVs, generating optimal adjustments based on predefined constraints. While many studies have explored MPC's effectiveness in AVs, most have been limited to simulations. Therefore, this study investigates the application of MPC in AVs through simulation and real-world testing. Alongside MPC, several other control algorithms, such as PID and Stanley, were also evaluated to compare their performance, considering the vehicle's kinematic and dynamic properties. Results from simulations and experiments indicate that MPC achieves precise trajectory tracking and facilitates smooth changes in direction, enhancing passenger comfort and safety.
KW - Autonomous Vehicles
KW - MPC Controller
KW - PID Controller
KW - Safety
KW - Stanley Controller
UR - http://www.scopus.com/inward/record.url?scp=85205108489&partnerID=8YFLogxK
U2 - 10.15866/ireaco.v17i3.24853
DO - 10.15866/ireaco.v17i3.24853
M3 - Article
AN - SCOPUS:85205108489
SN - 1974-6059
VL - 17
SP - 101
EP - 113
JO - International Review of Automatic Control
JF - International Review of Automatic Control
IS - 3
ER -