한국전산구조공학회논문집 (Journal of the Computational Structural Engineering Institute of Korea)

  • 제36권4호
  • /
  • Pages.233-242
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  • 2023
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  • 1229-3059(pISSN)
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  • 2287-2302(eISSN)
한국전산구조공학회 (Computational Structural Engineering Institute of Korea)
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다중 분산점 칼만필터를 이용한 급격한 구조손상 탐지 기법 개발

Unscented Kalman Filter with Multiple Sigma Points for Robust System Identification of Sudden Structural Damage

  • 이세혁 (한국건설기술연구원 구조연구본부 ) ;
  • 이상리 (캘리포니아대학교 토목환경공학과 ) ;
  • 이진호 (부경대학교 해양공학과 )
  • Se-Hyeok Lee (Department of Structural Engineering Research, Korea Institute of Civil Engineering and Building Technology) ;
  • Sang-ri Yi (Department of Civil and Environmental Engineering, University of California) ;
  • Jin Ho Lee (Department of Ocean Engineering, Pukyong National University)
  • 투고 : 2023.06.13
  • 심사 : 2023.07.06
  • 발행 : 2023.08.31
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 다중 시그마포인트 세트(MSP)를 사용하는 분산점 칼만필터(UKF)인 UKF-MSP를 소개한다. 비선형 동적시스템을 표현하기 위해 널리 알려진 Bouc-Wen 모델을 사용하였고, 비선형성 고려가 가능한 칼만필터 중 UKF를 선정하였다. 그런데 UKF는 두 가지 인공오차와 시그마포인트의 분포를 결정하는 스케일링 파라미터의 값을 튜닝(Tuning)하는 과정을 통해 적절히 설정해야만 대상 동적시스템의 추정하고자 하는 상태(State)를 정확히 추정할 수가 있다. 본 논문에서는 후자의 스케일링 파라미터 설정 문제를 완화하고자 하였으며, MSP를 사용함으로써 기존 UKF에 비해 칼만필터 튜닝 과정에 덜 민감한 UKF-MSP를 제안하였다. 지진으로 인한 급격한 구조손상 시나리오에 대해 UKF-MSP의 안정성을 검증하였다. 제안된 방법은 튜닝과정을 완화함과 동시에 다른 칼만필터 파라미터인 인공오차에 대해서도 덜 민감한 거동을 보임을 확인하였다.

The unscented Kalman filter (UKF), which is widely used to estimate the states of nonlinear dynamic systems, can be improved to realize robust system identification by using multiple sigma-point sets. When using Kalman filter methods for system identification, artificial noises must be appropriately selected to achieve optimal estimation performance. Additionally, an appropriate scaling factor for the sigma-points must be selected to capture the nonlinearity of the state-space model. This study entailed the use of Bouc-Wen hysteresis model to examine the nonlinear behavior of a single-degree-of-freedom oscillator. On the basis of the effects of the selected artificial noises and scaling factor, a new UKF method using multiple sigma-point sets was devised for improved robustness of the estimation over various signal-to-noise-ratio values. The results demonstrate that the proposed method can accurately track nonlinear system states even when the measurement noise levels are high, while being robust to the selection of artificial noise levels.

키워드

과제정보

이 논문은 한국건설기술연구원 2023년 구조연구본부 목적형 R&R 과제 "국민 안전과 건강한 인프라 환경을 위한 지속 가능한 인프라 구조기술 연구"에 의하여 연구되었습니다.

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