An unscented Kalman filter method for real time input-parameter-state estimation

TL;DR

This paper introduces a novel unscented Kalman filter approach for real-time estimation of system states, parameters, and unknown inputs in both linear and nonlinear systems, enhancing system understanding and identification.

Contribution

It presents a new two-stage unscented Kalman filter method for joint real-time estimation of states, parameters, and inputs, with demonstrated system identifiability.

Findings

Effective in linear and nonlinear systems

Can identify systems with zero or known inputs

Provides joint estimation of states, parameters, and inputs

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.