Subsystem identification of multivariable feedback and feedforward systems

Xingye Zhang; Jesse B. Hoagg

doi:10.1016/j.automatica.2016.05.027

Subsystem identification of multivariable feedback and feedforward systems

Xingye Zhang
, Jesse B. Hoagg

Mechanical and Aerospace Engineering

Producción científica: Article › revisión exhaustiva

20 Citas (Scopus)

Resumen

We present a frequency-domain technique for identifying multivariable feedback and feedforward subsystems that are interconnected with a known subsystem. This subsystem identification algorithm uses closed-loop input–output data, but no other system signals are assumed to be measured. In particular, neither the feedback signal nor the outputs of the unknown subsystems are assumed to be measured. We use a candidate-pool approach to identify the feedback and feedforward transfer function matrices, while guaranteeing asymptotic stability of the identified closed-loop transfer function matrix. The main analytic result shows that if the data noise is sufficiently small and the candidate pool is sufficiently dense, then the parameters of the identified feedback and feedforward transfer function matrices are arbitrarily close to the true parameters.

Idioma original	English
Páginas (desde-hasta)	131-137
Número de páginas	7
Publicación	Automatica
Volumen	72
DOI	https://doi.org/10.1016/j.automatica.2016.05.027
Estado	Published - oct 1 2016

Nota bibliográfica

Publisher Copyright:
© 2016 Elsevier Ltd

Financiación

This work was supported by the National Science Foundation through CMMI–1405257 . The material in this paper was not presented at any conference. This paper was recommended for publication in revised form by Associate Editor Er-Wei Bai under the direction of Editor Torsten Söderström.

Financiadores	Número del financiador
National Science Foundation Arctic Social Science Program	CMMI–1405257, 1405257

ASJC Scopus subject areas

Control and Systems Engineering
Electrical and Electronic Engineering

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title = "Subsystem identification of multivariable feedback and feedforward systems",

abstract = "We present a frequency-domain technique for identifying multivariable feedback and feedforward subsystems that are interconnected with a known subsystem. This subsystem identification algorithm uses closed-loop input–output data, but no other system signals are assumed to be measured. In particular, neither the feedback signal nor the outputs of the unknown subsystems are assumed to be measured. We use a candidate-pool approach to identify the feedback and feedforward transfer function matrices, while guaranteeing asymptotic stability of the identified closed-loop transfer function matrix. The main analytic result shows that if the data noise is sufficiently small and the candidate pool is sufficiently dense, then the parameters of the identified feedback and feedforward transfer function matrices are arbitrarily close to the true parameters.",

keywords = "Closed-loop system identification, Feedback and feedforward subsystems, Frequency-domain system identification, Stability, Subsystem identification",

author = "Xingye Zhang and Hoagg, \{Jesse B.\}",

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AU - Zhang, Xingye

AU - Hoagg, Jesse B.

PY - 2016/10/1

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N2 - We present a frequency-domain technique for identifying multivariable feedback and feedforward subsystems that are interconnected with a known subsystem. This subsystem identification algorithm uses closed-loop input–output data, but no other system signals are assumed to be measured. In particular, neither the feedback signal nor the outputs of the unknown subsystems are assumed to be measured. We use a candidate-pool approach to identify the feedback and feedforward transfer function matrices, while guaranteeing asymptotic stability of the identified closed-loop transfer function matrix. The main analytic result shows that if the data noise is sufficiently small and the candidate pool is sufficiently dense, then the parameters of the identified feedback and feedforward transfer function matrices are arbitrarily close to the true parameters.

AB - We present a frequency-domain technique for identifying multivariable feedback and feedforward subsystems that are interconnected with a known subsystem. This subsystem identification algorithm uses closed-loop input–output data, but no other system signals are assumed to be measured. In particular, neither the feedback signal nor the outputs of the unknown subsystems are assumed to be measured. We use a candidate-pool approach to identify the feedback and feedforward transfer function matrices, while guaranteeing asymptotic stability of the identified closed-loop transfer function matrix. The main analytic result shows that if the data noise is sufficiently small and the candidate pool is sufficiently dense, then the parameters of the identified feedback and feedforward transfer function matrices are arbitrarily close to the true parameters.

KW - Closed-loop system identification

KW - Feedback and feedforward subsystems

KW - Frequency-domain system identification

KW - Stability

KW - Subsystem identification

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DO - 10.1016/j.automatica.2016.05.027

M3 - Article

AN - SCOPUS:84989911018

SN - 0005-1098

VL - 72

SP - 131

EP - 137

JO - Automatica

JF - Automatica

ER -

Subsystem identification of multivariable feedback and feedforward systems

Resumen

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ASJC Scopus subject areas

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