A Study of Model Predictive Control for Autonomous Vehicle Path Tracking Control Regarding Passenger Safety Using MATLAB and ROS

Bambang Lelono Widjiantoro, Katherin Indriawati, Rida Ayu Arfianti, Kadek Dwi Wahyuadnyana

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Pages (from-to)101-113
Number of pages13
JournalInternational Review of Automatic Control
Volume17
Issue number3
DOIs
Publication statusPublished - 2024

Keywords

  • Autonomous Vehicles
  • MPC Controller
  • PID Controller
  • Safety
  • Stanley Controller

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