Optimization based 2D platoon control with multiple behaviors implemented through a finite state machine

Master thesis (2021)

Authors

S. Razdan Mechanical Engineering

Contributors

S. Wahls Team Sander Wahls - Mechanical, Maritime and Materials Engineering (mentor)
L. Ferranti Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering (mentor)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2021 Shuhul Razdan
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Published Date

28-07-2021

Language

English

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Faculty

Mechanical Engineering

Abstract

With inland transportation increasing every passing day, vehicle platooning offers a good solution towards travelling more efficiently. Along with reducing traffic congestion on roads, platooning also leads to better fuel consumption among vehicles, fewer accidents, and most importantly, vehicle platoons can be made autonomous using optimization-based control techniques, such as Model Predictive Control (MPC). Much research has been done towards optimise fuel consumption through slip-streaming and by using topological data. A separate research field also looks into performing lanes changes and avoiding obstacles. However, both these research fields are disjointed and use a different model and MPC formulations to employ control. This project aims at developing an unified system that can implement longitudinal control (fuel optimisation), formation reconfiguration (lane changes) and collision avoidance using one model and MPC formulation. The platoon switches between these behaviours depending on the environment around the platoon. This report also highlights the limitations of the controller and gives concrete recommendations on how to deal with the shortcomings.