Data-based subsystem identification for dynamic model updating

The accuracy of control and estimation tasks can strongly depend on the accuracy of the underlying model. In this paper, we consider a linear physical state space model subject to unmodeled dynamics in the state equation. In a first contribution, an optimal filter is outlined which yields estimates of the model error and the system states from measurements of the true system. In a second contribution, a technique is developed to update the model in case the unmodeled dynamics are arising from an unknown linear subsystem. In a third contribution, techniques are extended to nonlinear systems. Two illustrative simulation examples are included, a linear tape-drive modeling example and a nonlinear motor-pump example.