Heavy vehicle path control with neural networks - Heavy vehicle path control with neural networks

Examensarbete för masterexamen

Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.12380/255459
Download file(s):
File Description SizeFormat 
255459.pdfFulltext2.31 MBAdobe PDFView/Open
Type: Examensarbete för masterexamen
Master Thesis
Title: Heavy vehicle path control with neural networks - Heavy vehicle path control with neural networks
Authors: Insgård, Viktor
Jansson, Lucas
Abstract: This thesis explores the possibility of using neural networks for solving the path control problem, i.e. how to follow a predefined path as closely as possible. Two main approaches are used to achieve this, namely supervised learning and reinforcement learning. The supervised learning approach is based on existing path trackers which are used to generate data for the training procedure. The reinforcement learning uses a genetic algorithm and simulations to evaluate possible solutions. The supervised learning controllers are constructed as feed forward neural networks only, while the reinforcement learning controllers uses a recurrent neural network. The results shows that neural networks can be trained to solve the path tracking problem, both with supervised and reinforcement learning methods. Both the feed forward networks and the recurrent networks outperform the geometric path trackers. Further, a recurrent network was shown to perform better than a feed forward network, which indicates that the dynamical properties of such networks can be useful in path tracking applications.
Keywords: Informations- och kommunikationsteknik;Reglerteknik;Information & Communication Technology;Control Engineering
Issue Date: 2018
Publisher: Chalmers tekniska högskola / Institutionen för mekanik och maritima vetenskaper
Chalmers University of Technology / Department of Mechanics and Maritime Sciences
Series/Report no.: Master's thesis - Department of Mechanics and Maritime Sciences : 2018:27
URI: https://hdl.handle.net/20.500.12380/255459
Collection:Examensarbeten för masterexamen // Master Theses

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.