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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Bainier, G., Ponsart, JC., Marx, B. (2023). Anticipating the Loss of Unknown Input Observability for Sampled LPV Systems. In: Theilliol, D., Korbicz, J., Kacprzyk, J. (eds) Recent Developments in Model-Based and Data-Driven Methods for Advanced Control and Diagnosis. ACD 2022. Studies in Systems, Decision and Control, vol 467. Springer, Cham. https://doi.org/10.1007/978-3-031-27540-1_2
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