An adaptive nonlinear model predictive controller for longitudinal motion of automated vehicles

Martin Buechel; Alois Knoll

doi:10.1109/CCA.2016.7587829

An adaptive nonlinear model predictive controller for longitudinal motion of automated vehicles

Martin Buechel, Alois Knoll

nformatics 6 - Chair of IRobotics, Artificial Intelligence and Real-time Systems

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

8 Scopus citations

Abstract

This paper presents an Adaptive Nonlinear Model Predictive Controller for longitudinal motion of automated vehicles which incorporates advance information on future speed demand values, as well as on road grade changes. It is used in combination with a state and parameter estimator to adapt to a changing vehicle mass. This allows improved speed tracking capability for horizontal driving and steep hill climbing. Performance is explored through simulation of a driving scenario in a parking garage and shows encouraging improvements in quality of control. It could be shown that even though the controller is optimized for tracking performance, average fuel consumption can be reduced.

Original language	English
Title of host publication	2016 IEEE Conference on Control Applications, CCA 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	103-108
Number of pages	6
ISBN (Electronic)	9781509007554
DOIs	https://doi.org/10.1109/CCA.2016.7587829
State	Published - 10 Oct 2016
Event	2016 IEEE Conference on Control Applications, CCA 2016 - Buenos Aires, Argentina Duration: 19 Sep 2016 → 22 Sep 2016

Publication series

Name	2016 IEEE Conference on Control Applications, CCA 2016

Conference

Conference	2016 IEEE Conference on Control Applications, CCA 2016
Country/Territory	Argentina
City	Buenos Aires
Period	19/09/16 → 22/09/16

Access to Document

10.1109/CCA.2016.7587829

Cite this

Buechel, M., & Knoll, A. (2016). An adaptive nonlinear model predictive controller for longitudinal motion of automated vehicles. In 2016 IEEE Conference on Control Applications, CCA 2016 (pp. 103-108). Article 7587829 (2016 IEEE Conference on Control Applications, CCA 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCA.2016.7587829

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title = "An adaptive nonlinear model predictive controller for longitudinal motion of automated vehicles",

abstract = "This paper presents an Adaptive Nonlinear Model Predictive Controller for longitudinal motion of automated vehicles which incorporates advance information on future speed demand values, as well as on road grade changes. It is used in combination with a state and parameter estimator to adapt to a changing vehicle mass. This allows improved speed tracking capability for horizontal driving and steep hill climbing. Performance is explored through simulation of a driving scenario in a parking garage and shows encouraging improvements in quality of control. It could be shown that even though the controller is optimized for tracking performance, average fuel consumption can be reduced.",

author = "Martin Buechel and Alois Knoll",

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year = "2016",

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doi = "10.1109/CCA.2016.7587829",

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Buechel, M & Knoll, A 2016, An adaptive nonlinear model predictive controller for longitudinal motion of automated vehicles. in 2016 IEEE Conference on Control Applications, CCA 2016., 7587829, 2016 IEEE Conference on Control Applications, CCA 2016, Institute of Electrical and Electronics Engineers Inc., pp. 103-108, 2016 IEEE Conference on Control Applications, CCA 2016, Buenos Aires, Argentina, 19/09/16. https://doi.org/10.1109/CCA.2016.7587829

An adaptive nonlinear model predictive controller for longitudinal motion of automated vehicles. / Buechel, Martin; Knoll, Alois.
2016 IEEE Conference on Control Applications, CCA 2016. Institute of Electrical and Electronics Engineers Inc., 2016. p. 103-108 7587829 (2016 IEEE Conference on Control Applications, CCA 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - This paper presents an Adaptive Nonlinear Model Predictive Controller for longitudinal motion of automated vehicles which incorporates advance information on future speed demand values, as well as on road grade changes. It is used in combination with a state and parameter estimator to adapt to a changing vehicle mass. This allows improved speed tracking capability for horizontal driving and steep hill climbing. Performance is explored through simulation of a driving scenario in a parking garage and shows encouraging improvements in quality of control. It could be shown that even though the controller is optimized for tracking performance, average fuel consumption can be reduced.

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An adaptive nonlinear model predictive controller for longitudinal motion of automated vehicles

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