Identifiability, controllability & observability detection.

Version 1.2.5 (42,8 ko) par Gerard Van Willigenburg
Highly efficient identifiability, controllability & observability detection of large-scale nonlinear dynamical systems.
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Mise à jour 13 sept. 2025

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Highly efficient identifiability, controllability & observability detection of large-scale nonlinear dynamical systems.
Software associated with the paper:
L.G. Van Willigenburg, J.D. Stiger, J. Molenaar, 2022, "Sensitivity matrices as keys to local structural system properties of large-scale nonlinear systems" Nonlinear Dynamics, a Springer Nature journal. A PDF can be downloaded from
https://gvw007.yolasite.com/resources/Sensitivities-as-keys-NonlinDyn-2022!Errata.pdf
Documentation: readme.m

Citation pour cette source

Gerard Van Willigenburg (2025). Identifiability, controllability & observability detection. (https://fr.mathworks.com/matlabcentral/fileexchange/106000-identifiability-controllability-observability-detection), MATLAB Central File Exchange. Extrait(e) le .

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Version Publié le Notes de version
1.2.5

Link updated
1.2.4

Link updated
1.2.3

figureFullScreen deleted figure used
1.2.2

Line 92 of fxusen.m updated to also include uxNtr. Makes a difference only when nx0=1 is selected as an input to nlacchs. Instead of local strong accessibility this checks local accessibility.
1.2.1

The number of samples ns making up the sensitivity matrices in nlacchs, nlident is by default set to nx, npx, the maximum if all but one input/parameter is "disconnected". Script exmrhs now also calls function nlacchs.
1.2.0

The number of default samples ns to build the sensitivity matrix in nlacchs is updated to not depend on the number of system inputs nu (since an arbitrary number of inputs may be disconnected causing uncontrollability).
1.1.0

Update readme, dnkwbl64 (correction obtained from Jinrae Kim), nlacchs (output argument uxNcr added).
1.0.0