This paper presents a novel algorithm for joint input-state-parameter estimation in structural dynamics. The algorithm is derived from an existing smoothing algorithm. In each time step, the system model adopted in the joint input-state-parameter estimation is linearized around the current state, yielding an algorithm similar to the extended Kalman filter. It is shown that adopting a time delay in the estimation can significantly reduce the estimation error, especially in case data originates from sensors that are not collocated with the estimated inputs. Analytical expressions for the sensitivities of the system matrices with respect to unknown parameters are derived for the case of a linear underlying state-space model. These sensitivities are derived for models expressed in physical coordinates, models expressed in modal coordinates, and modally reduced-order models with a quasi-static correction to account for the contribution of the out-of-band modes. The proposed methodology is verified using numerical simulations and validated using data obtained from a laboratory experiment on a steel beam with I-shaped cross section.

A wide variety of system inversion algorithms has been proposed in the literature, tackling the problem of force identification, parameter estimation, and state/response estimation. Recently, much focus has gone to recursive joint input-state estimation and joint inputstate-parameter estimation, where the forces applied to the structure, the corresponding system states, and its parameters are simultaneously estimated in a recursive fashion. In order to reduce the computational load, these techniques are frequently used in combination with modally reduced order models. This paper shows that a model order reduction can lead to large estimation errors in the system inversion caused by disregarding the contribution of the so-called out-of-band modes. A recently developed computationally efficient quasi-static correction technique, which can be used in state-space modeling, is implemented and evaluated for both joint input-state estimation and joint input-state-parameter estimation. The evaluation is based on numerical simulations. It is shown that the quasi-static correction significantly reduces the estimation errors introduced by the model order reduction. 4386 COMPDYN 2019 7 th ECCOMAS Thematic Conference on Computational Methods in Structural Dynamics and Earthquake Engineering M. Papadrakakis, M. Fragiadakis (eds.