Optimization-Based Iterative Learning Speed Control for Vehicle Test Procedures

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.
Original languageEnglish
Title of host publication9th IFAC Symposium on Advances in Automotive Control
Pages516-522
DOIs
Publication statusPublished - 2019
Event9th IFAC International Symposium on Advances in Automotive Control - Orléans, France
Duration: 23 Jun 201927 Jun 2019

Conference

Conference9th IFAC International Symposium on Advances in Automotive Control
Abbreviated titleAAC
CountryFrance
CityOrléans
Period23/06/1927/06/19

Keywords

  • automotive control
  • learning control
  • iterative improvement
  • optimal trajectory

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  • Activities

    • 1 Talk at conference or symposium

    Optimization-Based Iterative Learning Speed Control for Vehicle Test Procedures

    Stefan Lambert Hölzl (Speaker)
    26 Jun 2019

    Activity: Talk or presentationTalk at conference or symposiumScience to science

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