• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.4
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• pp.233-242
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• 2023

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.