Article | Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016 | Safe Active Learning of a High Pressure Fuel Supply System Linköping University Electronic Press Conference Proceedings
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Title:
Safe Active Learning of a High Pressure Fuel Supply System
Author:
Mark Schillinger: Bosch Engineering GmbH, Germany Benedikt Ortelt: Bosch Engineering GmbH, Germany Benjamin Hartmann: Bosch Engineering GmbH, Germany Jens Schreiter: Robert Bosch GmbH, Germany Mona Meister: Robert Bosch GmbH, Germany Duy Nguyen-Tuong: Robert Bosch GmbH, Germany Oliver Nelles: Automatic Control, Mechatronics, Department of Mechanical Engineering, University of Siegen, Germany
DOI:
10.3384/ecp17142286
Download:
Full text (pdf)
Year:
2018
Conference:
Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016
Issue:
142
Article no.:
041
Pages:
286-292
No. of pages:
7
Publication type:
Abstract and Fulltext
Published:
2018-12-19
ISBN:
978-91-7685-399-3
Series:
Linköping Electronic Conference Proceedings
ISSN (print):
1650-3686
ISSN (online):
1650-3740
Publisher:
Linköping University Electronic Press, Linköpings universitet


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When modeling technical systems as black-box models, it is crucial to obtain as much and as informative measurement data as possible in the shortest time while employing safety constraints. Methods for an optimized online generation of measurement data are discussed in the field of Active Learning. Safe Active Learning combines the optimization of the query strategy regarding model quality with an exploration scheme in order to maintain user defined safety constraints. In this paper, the authors apply an approach for Safe Active Learning based on Gaussian process models (GP models) to the high pressure fuel supply system of a gasoline engine. For this purpose, several enhancements of the algorithm are necessary. An online optimization of the GP models’ hyperparameters is implemented, where special measures are taken to avoid a safety-relevant overestimation. A proper risk function is chosen and the trajectory to the sample points is taken into account regarding the estimation of the samples feasibility. The algorithm is evaluated in simulation and at a test vehicle.

Keywords: machine learning, system identification, active learning, Gaussian process models, automotive applications

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Mark Schillinger, Benedikt Ortelt, Benjamin Hartmann, Jens Schreiter, Mona Meister, Duy Nguyen-Tuong, Oliver Nelles
Title:
Safe Active Learning of a High Pressure Fuel Supply System
DOI:
http://dx.doi.org/10.3384/ecp17142286
References:
No references available

Proceedings of The 9th EUROSIM Congress on Modelling and Simulation, EUROSIM 2016, The 57th SIMS Conference on Simulation and Modelling SIMS 2016

Author:
Mark Schillinger, Benedikt Ortelt, Benjamin Hartmann, Jens Schreiter, Mona Meister, Duy Nguyen-Tuong, Oliver Nelles
Title:
Safe Active Learning of a High Pressure Fuel Supply System
DOI:
https://doi.org10.3384/ecp17142286
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Citations:
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