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dc.contributor.authorDekker, Lukas
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date.accessioned2018-04-27T13:59:06Z
dc.date.available2018-04-27T13:59:06Z
dc.identifier.urihttp://hdl.handle.net/1974/24057
dc.description.abstractThis work describes and demonstrates, through simulation and field trials, a technique for autonomous wheeled vehicle path following that uses iterative learning control (ILC) performed in a feedback linearized space to augment a base feedback linearization (FBL) path-following controller. The goal of ILC is to iteratively adjust steering rate inputs to account for unmodelled vehicle dynamics, environmental disturbances, and extreme path geometries. One fundamental advantage of this approach is that ILC can be used without having to employ approximate linearization at every time step, rendering the approach easily implementable and computationally inexpensive when compared with traditional approaches. The technique was validated by performing field trials using large industrial-scale autonomous underground mining vehicles. The presented work not only demonstrates the underlying technique in the field on commercial vehicles, but also proposes and validates a method for parallel speed learning, wherein the speed can be adjusted over subsequent learning trials to improve productivity. Finally, a method for pre-learning through simulation prior to deployment in the field is introduced in order to reduce initial path-following errors.en_US
dc.language.isoenen_US
dc.relation.ispartofseriesCanadian thesesen
dc.rightsQueen's University's Thesis/Dissertation Non-Exclusive License for Deposit to QSpace and Library and Archives Canadaen
dc.rightsProQuest PhD and Master's Theses International Dissemination Agreementen
dc.rightsIntellectual Property Guidelines at Queen's Universityen
dc.rightsCopying and Preserving Your Thesisen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectIterative Learning Controlen_US
dc.subjectPath Followingen_US
dc.subjectAutonomous Vehicleen_US
dc.subjectField Roboticsen_US
dc.subjectIndustrial Scaleen_US
dc.subjectFeedback Linearizationen_US
dc.subjectWheeled Vehicleen_US
dc.subjectCenter Articulateden_US
dc.subjectILCen_US
dc.subjectMining Roboticsen_US
dc.titleIndustrial-Scale Autonomous Vehicle Path Following by Feedback Linearized Iterative Learning Controlen_US
dc.typethesisen
dc.description.degreeMaster of Applied Scienceen_US
dc.contributor.supervisorMarshall, Joshua
dc.contributor.departmentMechanical and Materials Engineeringen_US


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