Reinforcement learning-based control co-design of digital twin-enabled full-vehicle active suspension systems
- DOI
- 10.1007/s00158-026-04304-y
- Published
- 2026-04
- Container
- Structural and Multidisciplinary Optimization
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s00158-026-04304-y,
title = {Reinforcement learning-based control co-design of digital twin-enabled full-vehicle active suspension systems},
author = {Ying-Kuan Tsai and Yi-Ping Chen and Vispi Karkaria and Wei Chen},
year = {2026},
journal = {Structural and Multidisciplinary Optimization},
doi = {10.1007/s00158-026-04304-y},
url = {https://doi.org/10.1007/s00158-026-04304-y}
}RIS
TY - JOUR TI - Reinforcement learning-based control co-design of digital twin-enabled full-vehicle active suspension systems AU - Ying-Kuan Tsai AU - Yi-Ping Chen AU - Vispi Karkaria AU - Wei Chen PY - 2026 JO - Structural and Multidisciplinary Optimization DO - 10.1007/s00158-026-04304-y UR - https://doi.org/10.1007/s00158-026-04304-y ER -
APA
Tsai, Y., Chen, Y., Karkaria, V., & Chen, W. (2026). Reinforcement learning-based control co-design of digital twin-enabled full-vehicle active suspension systems. Structural and Multidisciplinary Optimization. https://doi.org/10.1007/s00158-026-04304-y
Source records
- crossref · retrieved 2026-09-25T19:37:27.164Z