The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Formulas for Data-Driven Control: Stabilization, Optimality, and Robustness
Abstract
In a paper by Willems et al., it was shown that persistently exciting data can be used to represent the input-output behavior of a linear system. Based on this fundamental result, we derive a parametrization of linear feedback systems that paves the way to solve important control problems using data-dependent linear matrix inequalities only. The result is remarkable in that no explicit system's matrices identification is required. The examples of control problems we solve include the state and output feedback stabilization, and the linear quadratic regulation problem. We also discuss robustness to noise-corrupted measurements and show how the approach can be used to stabilize unstable equilibria of nonlinear systems.
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Cites
Optimal Sampled-Data Control Systems
Chen, T., & Francis, B. A. (1995) · doi:10.1007/978-1-4471-3037-6 · Cited by 2,217
Cited by
Data-Driven Model Predictive Control With Stability and Robustness Guarantees
Berberich, J., Kohler, J., Muller, M. A., & Allgower, F. (2021) · IEEE Transactions on Automatic Control · doi:10.1109/tac.2020.3000182 · Cited by 803
Wang, X., Karimi, H. R., Shen, M., Liu, D., Li, L.-W., & Shi, J. (2022) · Neural Networks · doi:10.1016/j.neunet.2022.09.021 · Cited by 31
Metadata
- Authors
- De Persis, C., & Tesi, P.
- Venue
- IEEE Transactions on Automatic Control
- Published
- 2020-03
- Volume
- 65
- Issue
- 3
- Pages
- 909-924
- ISSN
- 0018-9286
- 1558-2523
- 2334-3303
- OpenAlex
- W2995764727
- Cited by
- 983
- References
- 1
- FWCI
- 50.66
- Type
- journal article
- Language
- en
- Open access
- closed
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- De Persis, C., & Tesi, P. (2020). Formulas for Data-Driven Control: Stabilization, Optimality, and Robustness. IEEE Transactions on Automatic Control. https://doi.org/10.1109/tac.2019.2959924
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