Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Philipp Maximilian Sieberg"'
Publikováno v:
Vehicles, Vol 5, Iss 4, Pp 1727-1742 (2023)
Since electric power steering has replaced hydraulic power steering in passenger cars, steering feedback has become a challenging task in steering system development. Test benches represent a valid approach for improving steering feedback since they
Externí odkaz:
https://doaj.org/article/f815d165393849aeb921253f79a3c5f5
Publikováno v:
Actuators, Vol 12, Iss 5, p 186 (2023)
Shorter available development times and fewer available vehicle prototypes have increased the subsystem-based investigation on test rigs within the automotive development process. Steering systems exhibit a direct interface to the driver, therefore,
Externí odkaz:
https://doaj.org/article/e18c7aaf16ea40a5be981578ba73d86b
Publikováno v:
Sensors, Vol 22, Iss 9, p 3513 (2022)
The use of virtual sensors in vehicles represents a cost-effective alternative to the installation of physical hardware. In addition to physical models resulting from theoretical modeling, artificial intelligence and machine learning approaches are i
Externí odkaz:
https://doaj.org/article/2eab3101f0964118b8d41f3e59db8f91
Publikováno v:
Materials, Vol 15, Iss 7, p 2358 (2022)
Understanding the acting wear mechanisms in many cases is key to predicting lifetime, developing models describing component behavior, or for the improvement of the performance of components under tribological loading. Conventionally scanning electro
Externí odkaz:
https://doaj.org/article/239a7c7ffb794c21966b011dec01f519
Publikováno v:
Applied Sciences, Vol 11, Iss 10, p 4687 (2021)
Considering automated driving, vehicle dynamics control systems are also a crucial aspect. Vehicle dynamics control systems serve as an important influence factor on safety and ride comfort. By reducing the driver’s responsibility through partially
Externí odkaz:
https://doaj.org/article/a3696472da8f429db529d483c390c0a2
Publikováno v:
IEEE Transactions on Intelligent Transportation Systems. 23:6337-6346
Data-driven models are obtained purely from data without complex theoretical modeling and without explicit model knowledge. This results in black box models whose traceability and reliability constitute a major challenge. This contribution addressed
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f642a9c854aabb7bb1adbb820f5483de
https://doi.org/10.1109/tits.2021.3055800
https://doi.org/10.1109/tits.2021.3055800
Publikováno v:
Advances in Intelligent Systems and Computing ISBN: 9783030558666
This work shows the application of artificial neural networks for the control task of the roll angle in passenger cars. The training of the artificial neural network is based on the specific actor-critic reinforcement learning training algorithm. It
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::193ada48c71d7f03d5cd3ba283c87421
https://doi.org/10.1007/978-3-030-55867-3_4
https://doi.org/10.1007/978-3-030-55867-3_4
In the context of automated driving, the control of vehicle dynamics is one of the important issues. In addition to conventional control strategies, algorithms with predictive working principles are particularly relevant here. Using mathematical mode
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2b174d3d4eb0345148ac2253cc33957c
https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85099879949
https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85099879949
Publikováno v:
ITSC
This article deals with the integration of a neural state estimator into a control environment. A pre-trained recurrent neural roll angle estimator is analyzed in a closed loop environment of a model predictive control. The roll estimator consists of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f61da357a2fe97fa720cddbcf852091c
https://doi.org/10.1109/itsc.2019.8917106
https://doi.org/10.1109/itsc.2019.8917106
Publikováno v:
ITSC
This article presents a hybrid state estimation using vehicle dynamics as an application. The knowledge about the dynamic states are essential in the vehicle. Ultimately, the built-in control algorithms are using these states to exploit safety, comfo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::687d9bc2c96588291f4bf2e3bcc386bc
https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85076804412
https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&origin=inward&scp=85076804412