A State and Output Sensitivity Controllability Approach for Structural Identifiability of Linear State Space Models
| Autor: | Carlos Mendez-Blanco, Leyla Özkan |
|---|---|
| Přispěvatelé: | Smart Process Operations and Control Lab, Control Systems, Cyber-Physical Systems Center Eindhoven, EIRES Eng. for Sustainable Energy Systems, EAISI High Tech Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Rank (linear algebra)
020209 energy MIMO 02 engineering and technology State (functional analysis) Controllability Nonlinear system 020401 chemical engineering 0202 electrical engineering electronic engineering information engineering Identifiability Applied mathematics State space Sensitivity (control systems) 0204 chemical engineering Mathematics |
| Zdroj: | CDC 59th IEEE Conference on Decision and Control (CDC 2020), 294-299 STARTPAGE=294;ENDPAGE=299;TITLE=59th IEEE Conference on Decision and Control (CDC 2020) |
| Popis: | In this paper structural identifiability of state space models, possibly nonlinear in parameters, is assessed by analyzing the controllability of the output sensitivities. Sensitivity analysis provides a mathematical setting to analyze parameter identifiability from a physically intuitive perspective. Both SISO and MIMO cases are treated; in the former case the output controllability matrix rank directly allows to draw conclusions on the model structural identifiability. In the latter case, the analysis requires special attention due to the ordering induced by the vector derivative. The approach is illustrated on a linear compartmental model. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|