A new residual-based Kalman filter for real time input–parameter–state estimation using limited output information. (1st October 2022) - Impraimakis, Marios; Smyth, Andrew W.
Abstract: The real time, output-only, and joint input–parameter–state estimation capabilities of a new residual-based Kalman filter (RKF) are examined herein with respect to limited information conditions. The filter is based on the residual of the predicted and measured dynamic state output, as well as on the residual of the system model estimation. The considered sensitivity analysis is developed using a real time sensitivity matrix formulated by the filtered dynamic states. Without loss of application generality, the examined systems are considered to be structural–mechanical systems, the measurements to be accelerations, and the system model to be the equation of motion. Highlights: A new filter is proposed based on the residual of state prediction and model error. The sensitivity analysis is performed using a real time matrix with filtered states. A new exponential factor promotes controlled and accurate convergence. An investigation is performed for various output information scenarios.