Sie befinden Sich nicht im Netzwerk der Universität Paderborn. Der Zugriff auf elektronische Ressourcen ist gegebenenfalls nur via VPN oder Shibboleth (DFN-AAI) möglich. mehr Informationen...
Ergebnis 22 von 21645
Mechanical systems and signal processing, 2022-02, Vol.165, p.108345, Article 108345
2022

Details

Autor(en) / Beteiligte
Titel
An efficient algorithm to test the observability of rational nonlinear systems with unmeasured inputs
Ist Teil von
  • Mechanical systems and signal processing, 2022-02, Vol.165, p.108345, Article 108345
Ort / Verlag
Berlin: Elsevier Ltd
Erscheinungsjahr
2022
Link zum Volltext
Quelle
Alma/SFX Local Collection
Beschreibungen/Notizen
  • This work proposes an efficient algorithm to examine the observability and identifiability of rational nonlinear systems in the presence of unmeasured and unknown inputs. The proposed algorithm allows for determining whether the dynamic states, unknown parameters and unmeasured inputs of a dynamical system can be, in theory, successfully identified from a given set of input–output measurements. The underlying theory of the algorithm is based on a further extension of the recently suggested extended Observability Rank Condition while focusing on rational instead of analytic nonlinearities. For the robust development of the algorithm, a power series based framework is established for computing the observability matrix efficiently. The occurring framework substantially alleviates the computational burden of the standard implementations of the extended Observability Rank Condition approaches, which allows for applications to real-world engineering systems that are often large and complex. Several examples of large-scale and high-complexity engineering structures are used to demonstrate the performance and capability of the algorithm. Furthermore, the proposed algorithm is used to investigate the feasibility of monitoring a sub-system that is independently separated from a full system under the introduction of additional unmeasured inputs. •An algorithm determines the observability/identifiability of systems with unknown inputs.•A power series-based framework computes the observability matrix robustly.•Numerical realizations and modular operations are used for efficient implementation.•Examples demonstrate the application of the algorithm to large engineering structures.•A MATLAB implementation of the algorithm is made available to the users.
Sprache
Englisch
Identifikatoren
ISSN: 0888-3270
eISSN: 1096-1216
DOI: 10.1016/j.ymssp.2021.108345
Titel-ID: cdi_proquest_journals_2601601574

Weiterführende Literatur

Empfehlungen zum selben Thema automatisch vorgeschlagen von bX