Robust Kalman filter for discrete-time systems with correlated noises

Souto, Rodrigo Fontes; Ishihara, J.Y.

doi:10.1109/med.2008.4602037

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…Based on the above statistics, is used to calculate the Kalman gain Shock and Vibration of discrete systems; this process has been shown by (34). These above methods do not contain selection process for an optimal gain.…”

Section: The Simulation Of Wind-induced Motions Of a High-risementioning

confidence: 99%

An Observer-Based Controller with a LMI-Based Filter against Wind-Induced Motion for High-Rise Buildings

Chen

Teng

et al. 2017

Shock and Vibration

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Active mass damper (AMD) control system is proposed for high-rise buildings to resist a strong wind. However, negative influence of noise in sensors impedes the application of AMD systems in practice. To reduce the adverse influence of noise on AMD systems, a Kalman filter and a linear matrix inequality-(LMI-) based filter are designed. Firstly, a ten-year return period fluctuating wind load is simulated by mixed autoregressive-moving average (MARMA) method, and its reliability is tested by wind speed power spectrum and correlation analysis. Secondly, a designed state observer with different filters uses wind-induced acceleration responses of a high-rise building as the feedback signal that includes noise to calculate control force in this paper. Finally, these methods are applied to a numerical example of a high-rise building and an experiment of a single span four-storey steel frame. Both numerical and experimental results are presented to verify that both Kalman filter and LMI-based filter can effectively suppress noise, but only the latter can guarantee the stability of AMD parameters.

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Section: The Simulation Of Wind-induced Motions Of a High-risementioning

confidence: 99%

An Observer-Based Controller with a LMI-Based Filter against Wind-Induced Motion for High-Rise Buildings

Chen

Teng

et al. 2017

Shock and Vibration

View full text Add to dashboard Cite

show abstract

“…This filter is established on the basis of H 2 estimation criterion [1], it requires to know the mechanism of the signal and the statistical properties of the noise, however, can only get the approximate model of the signal in many cases, sometimes unable to obtain the statistical characteristics of the signal, which limit the application of Kalman filter in a certain extent. In addition, Kalman filtering algorithm is the optimal filter for a system without uncertainties, but one of the problems with the Kalman filters is that they may not be robust against modeling uncertainties [2,3].…”

Section: Introductionmentioning

confidence: 99%