We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
Robust adaptive Kalman filter for structural performance assessment.
- Authors
Yi, Shenglun; Su, Tingli; Tang, ZhenYun
- Abstract
Summary: Structural performance assessment is a critical stage in the health monitoring for the maintenance and risk management of engineering structures, in which the interstory drift is a key indicator. In this paper, a robust adaptive Kalman filter is proposed for an interstory drift estimation problem to show the health condition of steel structures in the case that the statistics or internal dynamics describing the signals and measurements are not known precisely. More precisely, we build an adaptive current Jerk model (ACJM) where the model parameters are updated in each time step to presuppose the statistics characterization of the steel dynamic, while the unknown measurement noise covariance is adapted based on a fixed‐lag innovation with respect to measurements. Moreover, a robust adaptive Kalman filter is designed for the modelling uncertainties in each time increment by solving a minimax game: one player tries to select an "actual" model far from the proposed ACJM with an exponential decay tolerance, while its "hostile" player, namely, the optimum filter, is designed by minimizing the estimation error according to the selected "actual" model. Finally, some simulation and experimental results show the effectiveness of the proposed algorithm.
- Subjects
ADAPTIVE filters; INDUSTRIAL engineering; STRUCTURAL health monitoring; STRUCTURAL engineering; ENGINEERING management; KALMAN filtering
- Publication
International Journal of Robust & Nonlinear Control, 2024, Vol 34, Issue 9, p5966
- ISSN
1049-8923
- Publication type
Article
- DOI
10.1002/rnc.7302