Input Design Using the Upper Bound of Parameter Identification Error Based on Least Square Cost Function with the Unstructured Model Uncertainty

Abstract

For the control design, it is important to build a good model. To identify the model parameter exactly, it is necessary to use data which are less affected by the disturbance or modelling uncertainty. We propose that it is important to evaluate the upper bound of the parameter estimation error when the disturbance and the unstructured model uncertainty exist. It can be used to decide the optimal identification input and frequency weight for least square criterion.