@article{1e32f53e-36d0-4039-9aae-fbca42ae765d,
  abstract     = {{<p>This letter analyzes certainty-equivalence adaptive controllers for sampled-data systems through a data-driven Riccati equation. Extending a recent framework for data-driven adaptive control, the proposed formulation provides the sampled-data counterpart that explicitly accounts for intersample behavior via lifted cost representations. Sufficient conditions are derived to guarantee stability and robustness of the closed-loop system without explicit model knowledge. Furthermore, it is shown that the closed-loop performance approaches that of the model-based sampled-data LQR when a sufficiently rich data set is available.</p>}},
  author       = {{Gerdpratoom, Nuthasith and Rantzer, Anders and Yamamoto, Kaoru}},
  issn         = {{0167-6911}},
  keywords     = {{Certainty-equivalence control; Data-driven control; Intersample dynamics; Sampled-data systems}},
  language     = {{eng}},
  publisher    = {{Elsevier}},
  series       = {{Systems and Control Letters}},
  title        = {{Data-driven sampled-data LQR : Certainty-equivalence control via lifted cost and Riccati analysis}},
  url          = {{http://dx.doi.org/10.1016/j.sysconle.2026.106435}},
  doi          = {{10.1016/j.sysconle.2026.106435}},
  volume       = {{213}},
  year         = {{2026}},
}