Informativity of noisy data for structural properties of linear systems
| Autor: | Jaap Eising, Harry L. Trentelman |
|---|---|
| Přispěvatelé: | Systems, Control and Applied Analysis |
| Rok vydání: | 2021 |
| Předmět: |
Polynomial
Structural properties General Computer Science Rank (linear algebra) Computer science Mechanical Engineering Linear system System identification Linear systems Data driven analysis Set (abstract data type) Controllability Range (mathematics) Geometric approach Control and Systems Engineering Observability Electrical and Electronic Engineering Algorithm |
| Zdroj: | Systems and Control Letters, 158:105058. ELSEVIER SCIENCE BV |
| ISSN: | 0167-6911 |
| DOI: | 10.1016/j.sysconle.2021.105058 |
| Popis: | This paper deals with developing tests for checking whether an unknown system has certain structural properties. The tests that we are aiming at are in terms of noisy input–state–output data obtained from the unknown system. Since, in general, the data do not determine the unknown system uniquely, many systems are compatible with the same set of data. Therefore we cannot apply system identification and apply existing, model based, tests. Instead, we will use the concept of informativity, and establish tests for informativity of the given noisy data. We will do this for a range of system properties, among which strong observability and detectability and strong controllability and stabilizability. These informativity tests will be in terms of rank tests on polynomial matrices that can be constructed from the noisy data. We will also set up a geometric framework for informativity analysis. Within that framework we will give geometric tests for informativity for strong observability, observability, and left-invertibility. |
| Databáze: | OpenAIRE |
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