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Journal Articles Automatica Year : 2022

Sampled-Data Estimator for Nonlinear Systems with Uncertainties and Arbitrarily Fast Rate of Convergence

Abstract

We study a class of continuous-time nonlinear systems with discrete measurements, model uncertainty, and sensor noise. We provide an estimator of the state for which the observation error enjoys a variant of the exponential input-to-state stability property with respect to the model uncertainty and sensor noise. A valuable novel feature is that the overshoot term in this stability estimate only involves a recent history of uncertainty values. Also, the rate of exponential convergence can be made arbitrarily large by reducing the supremum of the sampling intervals. Our proof uses a recently developed trajectory based approach. We illustrate our work using a model for a pendulum whose suspension point is subjected to an unknown time-varying bounded horizontal oscillation.
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Dates and versions

hal-03891465 , version 1 (09-12-2022)

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Frédéric Mazenc, Michael Malisoff, Silviu-Iulian Niculescu. Sampled-Data Estimator for Nonlinear Systems with Uncertainties and Arbitrarily Fast Rate of Convergence. Automatica, 2022, 142, pp.110361. ⟨10.1016/j.automatica.2022.110361⟩. ⟨hal-03891465⟩
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