Subsystem identification for nonlinear model updating

We consider model updating by adding correction terms to the model equations in the state space form. Two classes of errors, namely model errors in the dynamics equation and model errors in the output equation, are considered. The model errors are assumed to arise from an unknown nonlinear subsystem. First, the states of the true system are estimated using unbiased minimum-variance filters. Next, the state estimates are used to obtain least squares estimates of the unmodeled terms. Finally, these least squares estimates are used to identify the correction subsystem. We discuss model updating for the case in which the unknown subsystem is either a static nonlinear function or a dynamic nonlinear system. A few illustrative examples are also provided