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Lateral Model Predictive Control for Over-Actuated Autonomous Vehicle
KTH, School of Electrical Engineering (EES), Automatic Control. (Integrated Transport Research Lab)
KTH, School of Electrical Engineering (EES), Automatic Control. (Integrated Transport Research Lab)
KTH, School of Electrical Engineering (EES), Automatic Control. (Integrated Transport Research Lab)
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL. (Integrated Transport Research Lab)
2017 (English)In: 2017 IEEE Intelligent Vehicles Symposium (IV), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 310-316, article id 7995737Conference paper, Published paper (Refereed)
Abstract [en]

In this paper, a lateral controller is proposed for an over-Actuated vehicle. The controller is formulated as a linear time-varying model predictive controller. The aim of the controller is to track a desired path smoothly, by making use of the vehicle crabbing capability (sideways movement) and minimizing the magnitude of curvature used. To do this, not only the error to the path is minimized, but also the error to the desired orientation and the control signals requests. The controller uses an extended kinematic model that takes into consideration the vehicle crabbing capability and is able to track not only kinematically feasible paths, but also plan and track over non-feasible discontinuous paths. Ackermann steering geometry is used to transform the control requests, curvature, and crabbing angle, to wheel angles. Finally, the controller performance is evaluated first by simulation and, after, by means of experimental tests on an over-Actuated autonomous research vehicle.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2017. p. 310-316, article id 7995737
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-213522DOI: 10.1109/IVS.2017.7995737ISI: 000425212700048Scopus ID: 2-s2.0-85028028719ISBN: 9781509048045 OAI: oai:DiVA.org:kth-213522DiVA, id: diva2:1138084
Conference
28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 11 June 2017 through 14 June 2017
Funder
TrenOp, Transport Research Environment with Novel PerspectivesIntegrated Transport Research Lab (ITRL)
Note

QC 20170904

Available from: 2017-09-04 Created: 2017-09-04 Last updated: 2018-03-14Bibliographically approved

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Pereira, Gonçalo CollaresSvensson, LarsLima, PedroMårtensson, Jonas
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CiteExportLink to record
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Output format
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