Generalized Framework of OKID for Linear State-Space Model Identification

Francesco Vicario (), Minh Q. Phan (), Richard W. Longman () and Raimondo Betti ()
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Francesco Vicario: Columbia University
Minh Q. Phan: Dartmouth College
Richard W. Longman: Columbia University
Raimondo Betti: Columbia University

A chapter in Modeling, Simulation and Optimization of Complex Processes HPSC 2015, 2017, pp 249-260 from Springer

Abstract: Abstract This paper presents a generalization of observer/Kalman filter identification (OKID). OKID is a method for the simultaneous identification of a linear dynamical system and the associated Kalman filter from input-output measurements corrupted by noise. OKID was originally developed at NASA as the OKID/ERA algorithm. Recent work showed that ERA is not the only way to complete the OKID process and paved the way to the generalization of OKID as an approach to linear system identification. As opposed to other approaches, OKID is explicitly formulated via state observers providing an intuitive interpretation from a control theory perspective. The extension of the OKID framework to more complex identification problems, including nonlinear systems, is also discussed.

Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-67168-0_20

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DOI: 10.1007/978-3-319-67168-0_20

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