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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.
Original language | English |
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Title of host publication | 9th IFAC Symposium on Advances in Automotive Control |
Pages | 516-522 |
DOIs | |
Publication status | Published - 2019 |
Event | 9th IFAC International Symposium on Advances in Automotive Control - Orléans, France Duration: 23 Jun 2019 → 27 Jun 2019 |
Conference
Conference | 9th IFAC International Symposium on Advances in Automotive Control |
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Abbreviated title | AAC |
Country/Territory | France |
City | Orléans |
Period | 23/06/19 → 27/06/19 |
Keywords
- automotive control
- learning control
- iterative improvement
- optimal trajectory
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Dive into the research topics of 'Optimization-Based Iterative Learning Speed Control for Vehicle Test Procedures'. Together they form a unique fingerprint.Activities
- 1 Talk at conference or symposium
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Optimization-Based Iterative Learning Speed Control for Vehicle Test Procedures
Stefan Lambert Hölzl (Speaker)
26 Jun 2019Activity: Talk or presentation › Talk at conference or symposium › Science to science