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Generalized Framework of OKID for Linear State-Space Model Identification

In: Modeling, Simulation and Optimization of Complex Processes HPSC 2015

Author

Listed:
  • Francesco Vicario

    (Columbia University
    Philips Research North America)

  • Minh Q. Phan

    (Dartmouth College)

  • Richard W. Longman

    (Columbia University)

  • Raimondo Betti

    (Columbia University)

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.

Suggested Citation

  • Francesco Vicario & Minh Q. Phan & Richard W. Longman & Raimondo Betti, 2017. "Generalized Framework of OKID for Linear State-Space Model Identification," Springer Books, in: Hans Georg Bock & Hoang Xuan Phu & Rolf Rannacher & Johannes P. Schlöder (ed.), Modeling, Simulation and Optimization of Complex Processes HPSC 2015, pages 249-260, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-67168-0_20
    DOI: 10.1007/978-3-319-67168-0_20
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