Interval state estimation for uncertain polytopic systems

Marx, Benoît; Ichalal, Dalil; Ragot, José

doi:10.1080/00207179.2019.1644455

Cited by 5 publications

(1 citation statement)

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“…One may also bound Δ 𝑘,𝑛,𝑚 inside a polytope, or more specifically between two matrices in a component-wise approach, which can be achieved by shifting the Lipschitz assumption from 𝐴(𝜃 (•)) and 𝐹 1 (𝜃 (•)) to 𝜃 (•) directly. Finally, to reconstruct the unknown input 𝑓 (𝑡) while taking into account the intersample behavior of 𝜃, it is possible to build a robust observer [22,13,12,7,11] for the discrete-time uncertain LPV system (19) taken with 𝑛 = 1 and 𝑚 = 0.…”

Section: An Exact Discretization Of Sampled Lpv Systems Subject To An...mentioning

confidence: 99%

Anticipating the Loss of Unknown Input Observability for Sampled LPV Systems

Bainier

Ponsart

Marx

2023

Studies in Systems, Decision and Control

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Given a continuous-time Linear Parameter-Varying (LPV) system with a sampled scheduling parameter and subject to an unknown input, this paper provides -under some Lipschitz assumptions -an exact discretization of an extended system which translates the sampled-data unknown input estimation problem into a discretetime LPV observer design problem with norm-bounded uncertainties. The bounds developed in this process account for the inter-sample behavior of the scheduling parameter, and allow for an estimation of some near-future observability Gramians, from which it is possible to lower bound the number of samples for which the unknown input is guaranteed to remain observable.

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