Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
GPT-4o
Gemini 2.5 Pro Pro
o3 Pro
GPT-4.1 Pro
DeepSeek R1 via Azure Pro
2000 character limit reached

Optimal robust exact first-order differentiators with Lipschitz continuous output (2404.05863v2)

Published 8 Apr 2024 in eess.SY and cs.SY

Abstract: The signal differentiation problem involves the development of algorithms that allow to recover a signal's derivatives from noisy measurements. This paper develops a first-order differentiator with the following combination of properties: robustness to measurement noise, exactness in the absence of noise, optimal worst-case differentiation error, and Lipschitz continuous output where the output's Lipschitz constant is a tunable parameter. This combination of advantageous properties is not shared by any existing differentiator. Both continuous-time and sample-based versions of the differentiator are developed and theoretical guarantees are established for both. The continuous-time version of the differentiator consists in a regularized and sliding-mode-filtered linear adaptive differentiator. The sample-based, implementable version is then obtained through appropriate discretization. An illustrative example is provided to highlight the features of the developed differentiator.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (21)
  1. L. Fraguela, M. T. Angulo, J. A. Moreno, and L. Fridman, “Design of a prescribed convergence time uniform Robust Exact Observer in the presence of measurement noise,” in Proc. IEEE Conf. on Decis. and Control, 2012, pp. 6615–6620.
  2. L. K. Vasiljevic and H. K. Khalil, “Differentiation with high-gain observers the presence of measurement noise,” in Conference on Decision and Control.   IEEE, 2006, pp. 4717–4722.
  3. J. Holloway and M. Krstic, “Prescribed-time observers for linear systems in observer canonical form,” IEEE Trans. Autom. Control, vol. 64, no. 9, pp. 3905–3912, 2019.
  4. Y. Orlov, R. I. Verdes Kairuz, and L. T. Aguilar, “Prescribed-Time Robust Differentiator Design Using Finite Varying Gains,” IEEE Control Syst. Lett., vol. 6, pp. 620–625, 2022.
  5. A. Levant, “Robust Exact Differentiation via Sliding Mode Technique,” Automatica, vol. 34, no. 3, pp. 379–384, 1998.
  6. E. Cruz-Zavala, J. A. Moreno, and L. M. Fridman, “Uniform robust exact differentiator,” IEEE Trans. Autom. Control, vol. 56, no. 11, pp. 2727–2733, 2011.
  7. R. Seeber, H. Haimovich, M. Horn, L. M. Fridman, and H. De Battista, “Robust exact differentiators with predefined convergence time,” Automatica, vol. 134, p. 109858, 2021.
  8. R. Seeber and H. Haimovich, “Optimal robust exact differentiation via linear adaptive techniques,” Automatica, vol. 148, p. 110725, 2023.
  9. R. Aldana-López, R. Seeber, H. Haimovich, and D. Gómez-Gutiérrez, “On inherent limitations in robustness and performance for a class of prescribed-time algorithms,” Automatica, vol. 158, p. 111284, 2023.
  10. R. Seeber, “Worst-case error bounds for the super-twisting differentiator in presence of measurement noise,” Automatica, vol. 152, p. 110983, 2023.
  11. J. A. Moreno, “Arbitrary-order fixed-time differentiators,” IEEE Trans. Autom. Control, vol. 67, no. 3, pp. 1543–1549, 2021.
  12. R. Aldana-López, R. Seeber, D. Gómez-Gutiérrez, M. T. Angulo, and M. Defoort, “A redesign methodology generating predefined-time differentiators with bounded time-varying gains,” International Journal of Robust and Nonlinear Control, vol. 33, no. 15, pp. 9050–9065, 2023.
  13. S. Diop, J. Grizzle, and F. Chaplais, “On numerical differentiation algorithms for nonlinear estimation,” in Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No. 00CH37187), vol. 2.   IEEE, 2000, pp. 1133–1138.
  14. A. Levant, “Finite differences in homogeneous discontinuous control,” IEEE Trans. Autom. Control, vol. 52, no. 7, pp. 1208–1217, 2007.
  15. ——, “Chattering analysis,” IEEE Trans. Autom. Control, vol. 55, no. 6, pp. 1380–1389, 2010.
  16. V. Utkin, “Discussion aspects of high-order sliding mode control,” IEEE Trans. Autom. Control, vol. 61, no. 3, pp. 829–833, 2015.
  17. H. Haimovich, R. Seeber, R. Aldana-López, and D. Gómez-Gutiérrez, “Differentiator for noisy sampled signals with best worst-case accuracy,” IEEE Control Systems Letters, vol. 6, pp. 938–943, 2021.
  18. B. Brogliato and A. Polyakov, “Digital implementation of sliding-mode control via the implicit method: A tutorial,” International Journal of Robust and Nonlinear Control, vol. 31, no. 9, pp. 3528–3586, 2021.
  19. J. E. Carvajal-Rubio, J. D. Sánchez-Torres, M. Defoort, M. Djemai, and A. G. Loukianov, “Implicit and explicit discrete-time realizations of homogeneous differentiators,” International Journal of Robust and Nonlinear Control, vol. 31, no. 9, pp. 3606–3630, 2021.
  20. A. Hanan, A. Levant, and A. Jbara, “Low-chattering discretization of homogeneous differentiators,” IEEE Trans. Autom. Control, vol. 67, no. 6, pp. 2946–2956, 2021.
  21. R. Aldana-López, R. Seeber, H. Haimovich, and D. Gómez-Gutiérrez, “Exact differentiator with Lipschitz continuous output and optimal worst-case accuracy under bounded noise,” in 2023 62nd IEEE Conference on Decision and Control (CDC).   IEEE, 2023, pp. 7874–7880.
Citations (1)

Summary

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

Dice Question Streamline Icon: https://streamlinehq.com

Follow-up Questions

We haven't generated follow-up questions for this paper yet.