ACD 2022 Paper Abstract

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Paper WeA1.2

Bainier, Gustave (Université de Lorraine), Ponsart, Jean-Christophe (Université de Lorraine, CNRS), Marx, Benoit (Université de Lorraine)

Anticipating the Loss of Unknown Input Observability for Sampled LPV Systems

Scheduled for presentation during the Regular Session "Observers and Estimation" (WeA1), Wednesday, November 16, 2022, 16:20−16:40, MAIN ROOM - E406

16th European Workshop on Advanced Control and Diagnosis, November 16-18, 2022, Nancy, France

This information is tentative and subject to change. Compiled on March 28, 2024

Keywords Nonlinear and Hybrid Systems, Model-Based Diagnosis of Linear, Reliability and Safety

Abstract

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 discrete-time 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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