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Stability Conditions for Linear Time-Varying Model Predictive Control in Autonomous Driving
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-6802-7520
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-3672-5316
KTH, School of Electrical Engineering (EES), Automatic Control. KTH, School of Electrical Engineering (EES), Centres, ACCESS Linnaeus Centre.ORCID iD: 0000-0002-1927-1690
2017 (English)In: 2017 IEEE 56th Annual Conference on Decision and Control, CDC 2017, IEEE, 2017, p. 2775-2782Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents stability conditions when designing a linear time-varying model predictive controller for lateral control of an autonomous vehicle. Stability is proved via Lyapunov techniques by adding a terminal state constraint and a terminal cost. We detail how to compute the terminal state and the terminal cost for the linear time-varying case, and interpret the obtained results in the light of an autonomous driving application. To determine the stability conditions, the concept of multi-model description is used, where the linear time-varying model is separated into a finite number of time- invariant models that depend on a single parameter. The terminal set is the maximum positive invariant set of the multi- model description and the terminal cost is the result of a min-max optimization that determines the worst time-invariant model if used as a prediction model. In fact, in the autonomous driving case, we show that the min-max approach is a convex optimization problem. The stability conditions are computed offline, maintain the convexity of the optimization, and do not affect the execution time of the controller. In simulation, we demonstrate the stabilizing effectiveness of the proposed conditions through an illustrative example of path following with a heavy-duty vehicle. 

Place, publisher, year, edition, pages
IEEE, 2017. p. 2775-2782
Series
IEEE Conference on Decision and Control, ISSN 0743-1546
Keywords [en]
Autonomous Driving, Model Predictive Control, Stability
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:kth:diva-220576DOI: 10.1109/CDC.2017.8264062ISI: 000424696902110Scopus ID: 2-s2.0-85046277035ISBN: 978-1-5090-2873-3 (print)OAI: oai:DiVA.org:kth-220576DiVA, id: diva2:1169480
Conference
56th IEEE Annual Conference on Decision and Control, CDC 2017, Melbourne Convention and Exhibition Centre (MCEC)Melbourne, Australia, 12 December 2017 through 15 December 2017
Funder
VINNOVA
Note

QC 20180119

Available from: 2017-12-27 Created: 2017-12-27 Last updated: 2018-06-01Bibliographically approved

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fulltext(1241 kB)144 downloads
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Lima, Pedro F.Mårtensson, JonasWahlberg, Bo

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