Experimental validation of universal filtering and smoothing for joint input-state estimation of linear systems with adaptive tuning

In Kalman filtering, unknown inputs are often estimated by augmenting the state vector, which introduces reliance on fictitious input models. In contrast, minimum-variance unbiased methods estimate inputs and states separately, avoiding fictitious models but requiring strict sensor configurations, such as full-rank feedforward matrices or without direct feedthrough. To address these limitations, two universal approaches have been proposed to handle systems with or without direct feedthrough, including cases of rank-deficient feedforward matrices. Numerical studies have shown their robustness and applicability, however, they have so far relied on offline tuning, and performance under physical sensor noise and structural uncertainties has not yet been experimentally validated. Contributing to this gap, this paper experimentally validates the universal methods on a five-storey shear frame subjected to shake table tests and multi-impact events. Both typical and rank-deficient conditions are considered. Furthermore, a self-tuning mechanism is introduced to replace impractical offline tuning and enable real-time adaptability. The findings of this paper provide strong evidence of the robustness and adaptability of the methods for structural health monitoring applications, particularly when sensor networks deviate from ideal configurations.