Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
144 tokens/sec
GPT-4o
7 tokens/sec
Gemini 2.5 Pro Pro
45 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
38 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Controller synthesis for input-state data with measurement errors (2402.04157v2)

Published 6 Feb 2024 in eess.SY, cs.SY, math.DS, and math.OC

Abstract: We consider the problem of designing a state-feedback controller for a linear system, based only on noisy input-state data. We focus on input-state data corrupted by measurement errors, which, albeit less investigated, are as relevant as process disturbances in applications. For energy and instantaneous bounds on these measurement errors, we derive linear matrix inequalities for controller design where the one for the energy bound is equivalent to robust stabilization of all systems consistent with the noisy data points via a common Lyapunov function.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (15)
  1. E. Fogel, “System identification via membership set constraints with energy constrained noise,” IEEE Trans. Autom. Contr., vol. 24, no. 5, pp. 752–758, 1979.
  2. C. De Persis and P. Tesi, “Formulas for data-driven control: Stabilization, optimality, and robustness,” IEEE Trans. Autom. Contr., vol. 65, no. 3, pp. 909–924, 2020.
  3. J. Berberich, C. W. Scherer, and F. Allgöwer, “Combining Prior Knowledge and Data for Robust Controller Design,” IEEE Trans. Autom. Contr., vol. 68, no. 8, pp. 4618 – 4633, 2023.
  4. J. Coulson, J. Lygeros, and F. Dörfler, “Distributionally robust chance constrained data-enabled predictive control,” IEEE Trans. Autom. Contr., vol. 67, no. 7, pp. 3289–3304, 2021.
  5. H. J. van Waarde, M. K. Camlibel, and M. Mesbahi, “From noisy data to feedback controllers: Nonconservative design via a matrix S-lemma,” IEEE Trans. Autom. Contr., vol. 67, no. 1, pp. 162–175, 2022.
  6. F. Celi, G. Baggio, and F. Pasqualetti, “Closed-form and robust expressions for data-driven LQ control,” Ann. Rev. Contr., vol. 56, p. 100916, 2023.
  7. A. Bisoffi, C. De Persis, and P. Tesi, “Data-driven control via Petersen’s lemma,” Automatica, vol. 145, p. 110537, 2022.
  8. T. Söderström, “Why are errors-in-variables problems often tricky?” in Eur. Contr. Conf., 2003, pp. 802–807.
  9. V. Cerone, D. Piga, and D. Regruto, “Set-membership error-in-variables identification through convex relaxation techniques,” IEEE Trans. Autom. Contr., vol. 57, no. 2, pp. 517–522, 2011.
  10. D. P. Bertsekas and I. B. Rhodes, “Recursive state estimation for a set-membership description of uncertainty,” IEEE Trans. Autom. Contr., vol. 16, no. 2, pp. 117–128, 1971.
  11. A. Bisoffi, C. De Persis, and P. Tesi, “Trade-offs in learning controllers from noisy data,” Sys. & Contr. Lett., vol. 154, p. 104985, 2021.
  12. J. Miller, T. Dai, and M. Sznaier, “Data-driven stabilizing and robust control of discrete-time linear systems with error in variables,” 2022, arXiv preprint arXiv:2210.13430.
  13. M. Abuabiah, V. Cerone, S. Pirrera, and D. Regruto, “A non-iterative approach to direct data-driven control design of MIMO LTI systems,” IEEE Access, vol. 11, pp. 121 671–121 687, 2023.
  14. L. Li, A. Bisoffi, C. De Persis, and N. Monshizadeh, “Controller synthesis from noisy-input noisy-output data,” 2024, arXiv preprint arXiv:2402.02588.
  15. B. R. Barmish, “Necessary and sufficient conditions for quadratic stabilizability of an uncertain system,” J. Opt. theory and appl., vol. 46, no. 4, pp. 399–408, 1985.
Citations (5)

Summary

We haven't generated a summary for this paper yet.