TY - GEN
T1 - A Spectral Decomposition Identification Algorithm for Structured State-Space Models
T2 - 2021 American Control Conference, ACC 2021
AU - Freigoun, Mohammad T.
AU - Tsakalis, Konstantinos S.
AU - Raupp, Gregory B.
N1 - Publisher Copyright:
© 2021 American Automatic Control Council.
PY - 2021/5/25
Y1 - 2021/5/25
N2 - Structured state-space (grey-box) identification using experimental input-output data remains the desired framework for modeling dynamic physical and semiphysical systems represented by (or simplified to) a set of linear differential equations of a predetermined structure. While grey-box models can rise with favorable statistical properties, solver initialization of classical methods and structural identifiability often pose a challenge to the user seeking satisfactory results. By assuming distinct poles and Zero-Order Hold intersample behavior of the underlying system, it is shown that the typical grey-box constrained optimization problem can be formulated into an easier one by solving constrained eigenvalue problems. Following the trend of existing literature, the proposed formulation relies on a consistent discrete-time black-box model (e.g., N4SID) to solve for a structured, continuous-time one. While can be entirely sufficient in easier cases, this method is best suited for initializing the classical prediction-error estimation method, hence relieving the user from the burden of solver initialization in the absence of prior knowledge.
AB - Structured state-space (grey-box) identification using experimental input-output data remains the desired framework for modeling dynamic physical and semiphysical systems represented by (or simplified to) a set of linear differential equations of a predetermined structure. While grey-box models can rise with favorable statistical properties, solver initialization of classical methods and structural identifiability often pose a challenge to the user seeking satisfactory results. By assuming distinct poles and Zero-Order Hold intersample behavior of the underlying system, it is shown that the typical grey-box constrained optimization problem can be formulated into an easier one by solving constrained eigenvalue problems. Following the trend of existing literature, the proposed formulation relies on a consistent discrete-time black-box model (e.g., N4SID) to solve for a structured, continuous-time one. While can be entirely sufficient in easier cases, this method is best suited for initializing the classical prediction-error estimation method, hence relieving the user from the burden of solver initialization in the absence of prior knowledge.
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U2 - 10.23919/ACC50511.2021.9483369
DO - 10.23919/ACC50511.2021.9483369
M3 - Conference contribution
AN - SCOPUS:85111944176
T3 - Proceedings of the American Control Conference
SP - 2836
EP - 2841
BT - 2021 American Control Conference, ACC 2021
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 25 May 2021 through 28 May 2021
ER -