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Progress Maximization Model Predictive Controller
KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.ORCID iD: 0000-0003-1673-2671
KTH, School of Industrial Engineering and Management (ITM), Centres, Integrated Transport Research Lab, ITRL.ORCID iD: 0000-0002-3672-5316
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Optimization and Systems Theory.ORCID iD: 0000-0002-1927-1690
2018 (English)In: 2018 21ST INTERNATIONAL CONFERENCE ON INTELLIGENT TRANSPORTATION SYSTEMS (ITSC), IEEE , 2018, p. 1075-1082Conference paper, Published paper (Refereed)
Abstract [en]

This paper addresses the problem of progress maximization (i.e., traveling time minimization) along a given path for autonomous vehicles. Progress maximization plays an important role not only in racing, but also in efficient and safe autonomous driving applications. The progress maximization problem is formulated as a model predictive controller, where the vehicle model is successively linearized at each time step, yielding a convex optimization problem. To ensure real-time feasibility, a kinematic vehicle model is used together with several linear approximations of the vehicle dynamics constraints. We propose a novel polytopic approximation of the 'g-g' diagram, which models the vehicle handling limits by constraining the lateral and longitudinal acceleration. Moreover, the tire slip angles are restricted to ensure that the tires of the vehicle always operate in their linear force region by limiting the lateral acceleration. We illustrate the effectiveness of the proposed controller in simulation, where a nonlinear dynamic vehicle model is controlled to maximize the progress along a track, taking into consideration possible obstacles.

Place, publisher, year, edition, pages
IEEE , 2018. p. 1075-1082
Series
IEEE International Conference on Intelligent Transportation Systems-ITSC, ISSN 2153-0009
National Category
Transport Systems and Logistics
Identifiers
URN: urn:nbn:se:kth:diva-244588ISI: 000457881301013Scopus ID: 2-s2.0-85060480601ISBN: 978-1-7281-0323-5 (print)OAI: oai:DiVA.org:kth-244588DiVA, id: diva2:1293247
Conference
21st IEEE International Conference on Intelligent Transportation Systems (ITSC), NOV 04-07, 2018, Maui, HI
Note

QC 20190304

Available from: 2019-03-04 Created: 2019-03-04 Last updated: 2019-03-04Bibliographically approved

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Lima, Pedro F.Pereira, Goncalo CollaresMårtensson, JonasWahlberg, Bo

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