An unscented Kalman filter method for real time input-parameter-state estimation
This work addresses the need for improved system understanding in fields like control or monitoring by providing a real-time estimation method, though it appears incremental as it builds on existing unscented Kalman filter techniques.
The paper tackled the problem of real-time input-parameter-state estimation for linear and nonlinear systems using a novel unscented Kalman filter, demonstrating through perturbation analysis that systems with known inputs can be uniquely identified, enabling joint estimation of dynamic states, parameters, and inputs in real-time.
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.