Open AccessMapping prior information onto LMI eigenvalue‐regions for discrete‐time subspace identification
Author(s) -
Ricco Rodrigo A.,
Teixeira Bruno O.S.
Publication year - 2020
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2018.5623
Subject(s) - subspace topology , linear matrix inequality , eigenvalues and eigenvectors , identification (biology) , control theory (sociology) , overshoot (microwave communication) , state space , convex optimization , mathematics , state space representation , regular polygon , discrete time and continuous time , system identification , settling time , computer science , mathematical optimization , algorithm , data modeling , artificial intelligence , mathematical analysis , statistics , control (management) , step response , physics , engineering , biology , telecommunications , geometry , quantum mechanics , botany , control engineering , database
In subspace identification, prior information can be used to constrain the eigenvalues of the estimated state‐space model by defining corresponding linear matrix inequality (LMI) regions. In this study, first the authors argue on what kind of practical information can be extracted from historical data or step‐response experiments to possibly improve the dynamical properties of the corresponding model and, also, on how to mitigate the effect of the uncertainty on such information. For instance, prior knowledge regarding the overshoot, the period between damped oscillations and settling time may be useful to constrain the possible locations of the eigenvalues of the discrete‐time model. Then, they show how to map the prior information onto LMI regions and, when the obtaining regions are non‐convex, to obtain convex approximations.