On-line system identification in real time using a minicomputer

Sinha, Ν.Κ.; Tang, Mary Y.

doi:10.1016/0378-4754(77)90003-9

Cited by 1 publication

(2 citation statements)

References 13 publications

(12 reference statements)

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“…It needs to be mentioned that the Eqs. (3)(4) are nonlinear hence it is not possible to solve them via the linear observer such as linear Kalman filer (KF). Therefore, in order to solve the discussed problem a nonlinear observer namely the unscented Kalman filter (UKF) is employed.…”

Section: Description Of the Problemmentioning

confidence: 99%

See 1 more Smart Citation

Damping Matrix Identification for Structural Health Monitoring Considering Sensitivity of Initial Unknown Covariance

Miah¹,

Kaliske²,

Miah³

2019

World Congress on Civil, Structural, and Environmental Engineering

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The use of smart material and systems are gaining serious attention in the area of structural engineering due to their superior performance in real-world applications. The aforementioned technology requires real-time feedback in order to monitor them safely. This problem even gets into more complicated situation when it comes to adjust the current uncertainty of structures. To do this end, the realtime parameter estimation as well as state update (e.g. displacement and velocity) of an eight-storey frame type structure is considered. To deal with the early mentioned nonlinear problem due to multiple unknown variables a nonlinear observer is employed, namely, the unscented Kalman filter (UKF). As the parameter estimation along with the sate update are not that straight forward task due to the sensitivity of the initial covariance of the unknown parameters as well as for states uncertainties. The outcome shows that the proposed approach is capable of estimating the unknown parameters (e.g. damping matrix) quite efficiently with high accuracy. The investigated approach will facilitate the overall health monitoring of dynamical systems.

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Section: Description Of the Problemmentioning

confidence: 99%

“…The usefulness of the real-time parameters/system identification has been reported by many researchers such as [4]- [5][7] for various applications. For instance, in [2] investigated the possibility of system identification by using a minicomputer.…”

Section: Introductionmentioning

confidence: 99%