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Model-free intelligent Control for anti-lock braking systems on rough terrain
Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2022.
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2022
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| _version_ | 1867613525704179712 |
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| access_status_str | Open Access |
| author2 | Botha, Theunis R. |
| author_browse | Botha, Theunis R. |
| author_facet | Botha, Theunis R. |
| collection | Thesis |
| dc_rights_str_mv | © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2022. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/86433 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:37:32.074Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/86433 Model-free intelligent Control for anti-lock braking systems on rough terrain Botha, Theunis R. ricardo.deabreu@tuks.co.za Hamersma, Herman De Abreu, Ricardo Model-free Control Anti-lock braking system Reinforcement Learning Rough Terrain Off-road Vehicle Dynamics UCTD Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-11 SDG-11: Sustainable cities and communities Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production Dissertation (MEng (Mechanical Engineering))--University of Pretoria, 2022. Advances made in Advanced Driver Assistance Systems such as Antilock Braking Systems (ABS), have significantly improved the safety of road vehicles. ABS enhances the braking performance and steerability of a vehicle under severe braking conditions. However, ABS performance degrades on rough terrain. This is largely due to noisy measurements, the type of ABS control algorithm used, and the excitation of complex dynamics such as higher order tyre mode shapes that are neglected in the control strategy. This study proposes a model-free intelligent control technique with no modelling constraints that can overcome these un-modelled dynamics and parametric uncertainties. The Double Deep Q-learning Network algorithm with the Temporal Convolutional Network is presented as the intelligent control algorithm. The model is initially trained with a simplified single wheel model. The initial training data is transferred to and then enhanced by using a validated full-vehicle model including a physics-based tyre model, a 3D rough road profile with added stochasticity. The performance of the newly developed ABS controller is compared to a Bosch algorithm tuned for off-road use. Simulation results show a generalizable and robust control algorithm that can prevent wheel lockup over rough terrain without significantly deteriorating the vehicle’s stopping distance on smooth roads mi2025 Mechanical and Aeronautical Engineering MEng (Mechanical Engineering) Unrestricted SDG-09: Industry, innovation and infrastructure SDG-11: Sustainable cities and communities SDG-12: Responsible consumption and production 2022-07-25T10:45:47Z 2022-07-25T10:45:47Z 2022-09-07 2022 Dissertation * S2022 https://repository.up.ac.za/handle/2263/86433 DOI: 10.25403/UPresearchdata.20363601 https://doi.org/10.25403/UPresearchdata.20363601 en © 2022 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | Model-free Control Anti-lock braking system Reinforcement Learning Rough Terrain Off-road Vehicle Dynamics UCTD Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-11 SDG-11: Sustainable cities and communities Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production Model-free intelligent Control for anti-lock braking systems on rough terrain |
| title | Model-free intelligent Control for anti-lock braking systems on rough terrain |
| title_full | Model-free intelligent Control for anti-lock braking systems on rough terrain |
| title_fullStr | Model-free intelligent Control for anti-lock braking systems on rough terrain |
| title_full_unstemmed | Model-free intelligent Control for anti-lock braking systems on rough terrain |
| title_short | Model-free intelligent Control for anti-lock braking systems on rough terrain |
| title_sort | model free intelligent control for anti lock braking systems on rough terrain |
| topic | Model-free Control Anti-lock braking system Reinforcement Learning Rough Terrain Off-road Vehicle Dynamics UCTD Engineering, built environment and information technology theses SDG-09 SDG-09: Industry, innovation and infrastructure Engineering, built environment and information technology theses SDG-11 SDG-11: Sustainable cities and communities Engineering, built environment and information technology theses SDG-12 SDG-12: Responsible consumption and production |
| url | https://repository.up.ac.za/handle/2263/86433 https://doi.org/10.25403/UPresearchdata.20363601 |