Online Learning of Tire Behaviour combined with MPC for Autonomous Racing

Master thesis (2020)

Authors

V.K. Iyer Mechanical Engineering

Contributors

B. Shyrokau Intelligent Vehicles - Mechanical, Maritime and Materials Engineering (mentor)
M. Wisse Robot Dynamics - Mechanical, Maritime and Materials Engineering (graduation committee member)
W. Pan Robot Dynamics - Mechanical, Maritime and Materials Engineering (graduation committee member)
Valentin Ivanov Ilmenau University of Technology (graduation committee member)
Y. Zheng (graduation committee member)
Faculty
Mechanical Engineering, Mechanical Engineering
Copyright: © 2020 Kunal Iyer
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Published Date

26-08-2020

Language

English

Reuse Rights

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Faculty

Mechanical Engineering

Abstract

In this project, a unique method of combining online learning with model predictive control is applied to autonomous racing. A concern in autonomous racing is that accurate models that encapsulate the dynamics of the vehicle are complex, nonlinear, and difficult to identify. In order to make this more practical for control purposes, the controller is initialized with a nominal tire model, which then learns tire properties online using locally weighted projection regression during the course of the race. This makes it more practical for control purposes while maintaining model accuracy. Focus is placed on learning the tire properties which in reality, keep varying due to wear, temperature and pressure fluctuations, etc. The main objective is to minimize lap times by allowing the controller to "learn" its varying tire behavior while on the track.

Files

Thesis_report_Kunal_Iyer.pdf
(.pdf | 2.91 Mb)
- Embargo expired in 04-08-2023