Real-time nonlinear structural system identification via iterated unscented Kalman filter

Structural system identification has attracted much attention in the structural dynamics field over the past decades. The unscented Kalman filter (UKF) is often used to deal with nonlinear system identification in civil engineering field. In practices, applying a UKF to highly nonlinear structural systems is not a trivial task, particularly those subject to severe loading. Recently, a new technique, the iterated unscented Kalman filter (IUKF) is applicable to highly nonlinear systems. In this paper, the IUKF is applied for nonlinear structural system identification (NSSI). Experimental results show that the IUKF produces better state estimation and parameter identification than the UKF, and the IUKF is also more robust to measurement noise levels.