Experimental validation of universal filtering and smoothing for linear system identification using adaptive tuning

2025-08-20

AI Summary

Conventional Kalman filtering and minimum-variance unbiased (MVU) estimation fail in structural health monitoring when sensor configurations are non-ideal—e.g., rank-deficient feedthrough matrices, direct transmission paths, or unmodeled noise/parameter uncertainties. Method: This paper proposes a general adaptive filtering and smoothing framework integrating MVU principles, state-augmented filtering, and online self-calibration—without requiring fictitious input models or full-rank feedthrough assumptions. Contribution/Results: Validated for the first time in physical experiments using a five-story shear-frame shake-table setup under multiple impact excitations, the method demonstrates robustness and real-time adaptability under realistic sensor noise and structural parameter uncertainty. It achieves high-accuracy joint estimation of system states and unknown inputs, significantly extending the engineering applicability boundary of general unknown-input estimation methods.

Technology Category

• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

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.

Problem

• Validates universal filtering for systems with rank-deficient feedthrough
• Addresses offline tuning limitation through self-tuning mechanism
• Tests robustness under physical sensor noise and structural uncertainties

Innovation

• Universal filtering for systems with rank-deficient feedthrough
• Self-tuning mechanism replacing offline calibration
• Experimental validation on shear frame under impacts