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Research Papers

Identification of Non-Gaussian Stochastic System

[+] Author and Article Information
Sung-man Park

Department of Control
and Instrumentation Engineering,
Korea University,
Seoul, Korea 136-701
e-mail: yamjun99@korea.ac.kr

O-shin Kwon

LG Electronics Ltd. Co,
Seoul, Korea 153-802
e-mail: oshin.kwon@lge.com

Jin-sung Kim

LG Electronics Ltd. Co,
Seoul, Korea 153-802
e-mail: jinsung83.kim@lge.com

Jong-bok Lee

Hyun-dai Motors Ltd. Co,
Seoul, Korea 445-706
e-mail: jongbok_lee@hyundai.com

Hoon Heo

Department of Control
and Instrumentation Engineering,
Korea University,
Seoul, Korea 137-701
e-mail: heo257@korea.ac.kr

Contributed by the Dynamic Systems Division of ASME for publication in the JOURNAL OF DYNAMIC SYSTEMS, MEASUREMENT, AND CONTROL. Manuscript received May 16, 2010; final manuscript received January 15, 2014; published online April 4, 2014. Assoc. Editor: Douglas Adams.

J. Dyn. Sys., Meas., Control 136(4), 041006 (Apr 04, 2014) (5 pages) Paper No: DS-10-1126; doi: 10.1115/1.4026516 History: Received May 16, 2010; Revised January 15, 2014

This paper proposes a method to identify non-Gaussian random noise in an unknown system through the use of a modified system identification (ID) technique in the stochastic domain, which is based on a recently developed Gaussian system ID. The non-Gaussian random process is approximated via an equivalent Gaussian approach. A modified Fokker–Planck–Kolmogorov equation based on a non-Gaussian analysis technique is adopted to utilize an effective Gaussian random process that represents an implied non-Gaussian random process. When a system under non-Gaussian random noise reveals stationary moment output, the system parameters can be extracted via symbolic computation. Monte Carlo stochastic simulations are conducted to reveal some approximate results, which are close to the actual values of the system parameters.

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References

Figures

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Fig. 5

m20 response of a system exposed to non-Gaussian random noise

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Fig. 6

m02 response of a system exposed to non-Gaussian random noise

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Fig. 7

Natural frequency ωn extracted via a non-Gaussian system ID

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Fig. 8

Damping ratio ς extracted via a non-Gaussian system ID

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Fig. 2

Non-Gaussian random noise input

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Fig. 3

Probability density function of non-Gaussian random noise input

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Fig. 4

m10 response of a system exposed to non-Gaussian random noise

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Fig. 1

The conceptual system in this study

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