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📄 Abstract
Abstract: The input-parameter-state estimation capabilities of a novel unscented Kalman
filter is examined herein on both linear and nonlinear systems. The unknown
input is estimated in two stages within each time step. Firstly, the predicted
dynamic states and the system parameters provide an estimation of the input.
Secondly, the corrected with measurements states and parameters provide a final
estimation. Importantly, it is demonstrated using the perturbation analysis
that, a system with at least a zero or a non-zero known input can potentially
be uniquely identified. This output-only methodology allows for a better
understanding of the system compared to classical output-only parameter
identification strategies, given that all the dynamic states, the parameters,
and the input are estimated jointly and in real-time.
Key Contributions
Presents a novel Unscented Kalman Filter (UKF) method for real-time, output-only estimation of unknown inputs, system parameters, and dynamic states. It demonstrates that systems with known or unknown inputs can be uniquely identified through a two-stage estimation process within each time step.
Business Value
Improves the ability to monitor, control, and diagnose complex systems in real-time, leading to enhanced performance, safety, and predictive maintenance in various engineering applications.