Data-driven linear parameter-varying modelling of the steering dynamics of an autonomous car Article Swipe
Gábor Rödönyi
,
Roland Tóth
,
Dániel Pup
,
Ádám Kisari
,
Zs. Vígh
,
Péter Kőrös
,
J. Bokor
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2021.08.575
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1016/j.ifacol.2021.08.575
Developing automatic driving solutions and driver support systems requires accurate vehicle specific models to describe and predict the associated motion dynamics of the vehicle. Despite of the mature understanding of ideal vehicle dynamics, which are inherently nonlinear, modern cars are equipped with a wide array of digital and mechatronic components that are difficult to model. Furthermore, due to manufacturing, each car has its personal motion characteristics which change over time. Hence, it is important to develop data-driven modelling methods that are capable to capture from data all relevant aspects of vehicle dynamics in a model that is directly utilisable for control. In this paper, we show how Linear Parameter-Varying (LPV) modelling and system identification can be applied to reliably capture personalised model of the steering system of an autonomous car based on measured data. Compared to other nonlinear identification techniques, the obtained LPV model is directly utilisable for powerful controller synthesis methods of the LPV framework.
Related Topics
Concepts
Mechatronics
Control engineering
Nonlinear system
Identification (biology)
System identification
Computer science
Controller (irrigation)
Control theory (sociology)
Vehicle dynamics
Dynamics (music)
System dynamics
Engineering
Control (management)
Data modeling
Artificial intelligence
Automotive engineering
Database
Quantum mechanics
Botany
Biology
Acoustics
Physics
Agronomy
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.ifacol.2021.08.575
- https://www.sciencedirect.com/science/article/pii/S2405896321013513?via%3Dihub
- OA Status
- diamond
- Cited By
- 7
- References
- 23
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3198956607
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3198956607Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.ifacol.2021.08.575Digital Object Identifier
- Title
-
Data-driven linear parameter-varying modelling of the steering dynamics of an autonomous carWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
-
Gábor Rödönyi, Roland Tóth, Dániel Pup, Ádám Kisari, Zs. Vígh, Péter Kőrös, J. BokorList of authors in order
- Landing page
-
https://doi.org/10.1016/j.ifacol.2021.08.575Publisher landing page
- PDF URL
-
https://www.sciencedirect.com/science/article/pii/S2405896321013513?via%3DihubDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
-
https://www.sciencedirect.com/science/article/pii/S2405896321013513?via%3DihubDirect OA link when available
- Concepts
-
Mechatronics, Control engineering, Nonlinear system, Identification (biology), System identification, Computer science, Controller (irrigation), Control theory (sociology), Vehicle dynamics, Dynamics (music), System dynamics, Engineering, Control (management), Data modeling, Artificial intelligence, Automotive engineering, Database, Quantum mechanics, Botany, Biology, Acoustics, Physics, AgronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
-
2024: 2, 2023: 2, 2022: 3Per-year citation counts (last 5 years)
- References (count)
-
23Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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