Optimization-Based Iterative Learning Speed Control for Vehicle Test Procedures

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

Abstract

Procedures for measuring the emissions of automotive vehicles typically include a speed trace that the driver has to track within prescribed tolerances. For development purposes, following this trace by means of automatic control is desirable in order to minimize costs. In this contribution, an iterative learning scheme is proposed that iteratively improves a feed-forward control signal. This is done by means of an optimization problem that takes the speed tolerances into account in the form of constraints. Experimental results obtained with a vehicle on a Road-to-Rig (R2R) test bed for a part of the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) are presented and compared to results of a pure PI control scheme. After very few iterations, both tolerance violations and sudden changes of the pedal position are eliminated, yielding a significantly improved driving behavior.
Originalspracheenglisch
Titel9th IFAC Symposium on Advances in Automotive Control
Seiten516-522
DOIs
PublikationsstatusVeröffentlicht - 2019
Veranstaltung9th IFAC International Symposium on Advances in Automotive Control - Orléans, Frankreich
Dauer: 23 Jun 201927 Jun 2019

Konferenz

Konferenz9th IFAC International Symposium on Advances in Automotive Control
KurztitelAAC
LandFrankreich
OrtOrléans
Zeitraum23/06/1927/06/19

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Speed control
Feedforward control
Costs

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    Optimization-Based Iterative Learning Speed Control for Vehicle Test Procedures. / Seeber, Richard; Hölzl, Stefan Lambert; Bauer, Robert; Horn, Martin.

    9th IFAC Symposium on Advances in Automotive Control. 2019. S. 516-522.

    Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem KonferenzbandForschungBegutachtung

    Seeber, R, Hölzl, SL, Bauer, R & Horn, M 2019, Optimization-Based Iterative Learning Speed Control for Vehicle Test Procedures. in 9th IFAC Symposium on Advances in Automotive Control. S. 516-522, Orléans, Frankreich, 23/06/19. https://doi.org/10.1016/j.ifacol.2019.09.082
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    abstract = "Procedures for measuring the emissions of automotive vehicles typically include a speed trace that the driver has to track within prescribed tolerances. For development purposes, following this trace by means of automatic control is desirable in order to minimize costs. In this contribution, an iterative learning scheme is proposed that iteratively improves a feed-forward control signal. This is done by means of an optimization problem that takes the speed tolerances into account in the form of constraints. Experimental results obtained with a vehicle on a Road-to-Rig (R2R) test bed for a part of the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) are presented and compared to results of a pure PI control scheme. After very few iterations, both tolerance violations and sudden changes of the pedal position are eliminated, yielding a significantly improved driving behavior.",
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    N2 - Procedures for measuring the emissions of automotive vehicles typically include a speed trace that the driver has to track within prescribed tolerances. For development purposes, following this trace by means of automatic control is desirable in order to minimize costs. In this contribution, an iterative learning scheme is proposed that iteratively improves a feed-forward control signal. This is done by means of an optimization problem that takes the speed tolerances into account in the form of constraints. Experimental results obtained with a vehicle on a Road-to-Rig (R2R) test bed for a part of the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) are presented and compared to results of a pure PI control scheme. After very few iterations, both tolerance violations and sudden changes of the pedal position are eliminated, yielding a significantly improved driving behavior.

    AB - Procedures for measuring the emissions of automotive vehicles typically include a speed trace that the driver has to track within prescribed tolerances. For development purposes, following this trace by means of automatic control is desirable in order to minimize costs. In this contribution, an iterative learning scheme is proposed that iteratively improves a feed-forward control signal. This is done by means of an optimization problem that takes the speed tolerances into account in the form of constraints. Experimental results obtained with a vehicle on a Road-to-Rig (R2R) test bed for a part of the Worldwide Harmonized Light Vehicle Test Procedure (WLTP) are presented and compared to results of a pure PI control scheme. After very few iterations, both tolerance violations and sudden changes of the pedal position are eliminated, yielding a significantly improved driving behavior.

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