Skip to content
Free planNo credit card requiredStart writing

article · IEEE Transactions on Automatic Control · 2025

Iterative Model Learning and Dual Iterative Learning Control: A Unified Framework for Data-Driven Iterative Learning Control

Michael Meindl, Simon Bachhuber, Thomas Seel

Cited by 4Abstract and details only

Abstract

The index has no abstract for this paper. The publisher’s page may have one.

Cite this paper

APA 7

Meindl, M., Bachhuber, S., & Seel, T. (2025). Iterative Model Learning and Dual Iterative Learning Control: A Unified Framework for Data-Driven Iterative Learning Control. IEEE Transactions on Automatic Control, 70(12), 7818–7829. https://doi.org/10.1109/tac.2025.3577958

MLA 9

Meindl, Michael, et al. “Iterative Model Learning and Dual Iterative Learning Control: A Unified Framework for Data-Driven Iterative Learning Control.” IEEE Transactions on Automatic Control, vol. 70, no. 12, 2025, pp. 7818–29, https://doi.org/10.1109/tac.2025.3577958.

Chicago

Meindl, Michael, Simon Bachhuber, and Thomas Seel. 2025. “Iterative Model Learning and Dual Iterative Learning Control: A Unified Framework for Data-Driven Iterative Learning Control.” IEEE Transactions on Automatic Control 70 (12): 7818–29. https://doi.org/10.1109/tac.2025.3577958.

Harvard

Meindl, M., Bachhuber, S. and Seel, T. (2025) ‘Iterative Model Learning and Dual Iterative Learning Control: A Unified Framework for Data-Driven Iterative Learning Control’, IEEE Transactions on Automatic Control, 70(12), pp. 7818–7829. Available at: https://doi.org/10.1109/tac.2025.3577958.

BibTeX
@article{meindl2025iterative,
  title = {{Iterative Model Learning and Dual Iterative Learning Control: A Unified Framework for Data-Driven Iterative Learning Control}},
  author = {Meindl, Michael and Bachhuber, Simon and Seel, Thomas},
  journal = {IEEE Transactions on Automatic Control},
  volume = {70},
  number = {12},
  pages = {7818--7829},
  year = {2025},
  doi = {10.1109/tac.2025.3577958}
}

Made from the index record. Check names, capitalisation and page numbers against the paper. Need another style? AI for Research has over 10,000.