Smart Structures and Systems

  • 제17권6호
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  • Pages.903-915
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  • 2016
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Improved Kalman filter with unknown inputs based on data fusion of partial acceleration and displacement measurements

  • Liu, Lijun (Department of Civil Engineering, Xiamen University) ;
  • Zhu, Jiajia (Department of Civil Engineering, Xiamen University) ;
  • Su, Ying (Department of Civil Engineering, Xiamen University) ;
  • Lei, Ying (Department of Civil Engineering, Xiamen University)
  • 투고 : 2016.01.05
  • 심사 : 2016.04.16
  • 발행 : 2016.06.25
⟨ 이전 논문 다음 논문 ⟩

초록

The classical Kalman filter (KF) provides a practical and efficient state estimation approach for structural identification and vibration control. However, the classical KF approach is applicable only when external inputs are assumed known. Over the years, some approaches based on Kalman filter with unknown inputs (KF-UI) have been presented. However, these approaches based solely on acceleration measurements are inherently unstable which leads poor tracking and so-called drifts in the estimated unknown inputs and structural displacement in the presence of measurement noises. Either on-line regularization schemes or post signal processing is required to treat the drifts in the identification results, which prohibits the real-time identification of joint structural state and unknown inputs. In this paper, it is aimed to extend the classical KF approach to circumvent the above limitation for real time joint estimation of structural states and the unknown inputs. Based on the scheme of the classical KF, analytical recursive solutions of an improved Kalman filter with unknown excitations (KF-UI) are derived and presented. Moreover, data fusion of partially measured displacement and acceleration responses is used to prevent in real time the so-called drifts in the estimated structural state vector and unknown external inputs. The effectiveness and performance of the proposed approach are demonstrated by some numerical examples.

키워드

과제정보

연구 과제 주관 기관 : National Natural Science Foundation of China (NSFC)

참고문헌

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  4. KF-Based Multiscale Response Reconstruction under Unknown Inputs with Data Fusion of Multitype Observations vol.32, pp.4, 2016, https://doi.org/10.1061/(asce)as.1943-5525.0001031
  5. A Two-Step Drive-By Bridge Damage Detection Using Dual Kalman Filter vol.20, pp.10, 2016, https://doi.org/10.1142/s0219455420420067
  6. Investigations on state estimation of smart structure systems vol.25, pp.1, 2020, https://doi.org/10.12989/sss.2020.25.1.037
  7. Generalized algorithms for the identification of seismic ground excitations to building structures based on generalized Kalman filtering under unknown input vol.23, pp.10, 2016, https://doi.org/10.1177/1369433220906225
  8. Integration of identification and vibration control of time-varying structures subject to unknown seismic ground excitation vol.26, pp.15, 2016, https://doi.org/10.1177/1077546319896444
  9. Identifying Distributed Dynamic Loading in One Spatial Dimension Based on Combing Wavelet Decomposition and Kalman Filter with Unknown Input vol.34, pp.4, 2021, https://doi.org/10.1061/(asce)as.1943-5525.0001265
  10. Real-time simultaneous input-state-parameter estimation with modulated colored noise excitation vol.165, pp.None, 2022, https://doi.org/10.1016/j.ymssp.2021.108378