A Consideration of the Upper Bound of Parameter Identification Error with Unstructured Modelling Uncertainty and Disturbance

Abstract

When a control system is designed, it is important to make a model which can explain the input-output relation precisely. To identify the model’s parameter as precisely as possible, it is important to make the experimental signal less affected by the disturbance or the unstructured model uncertainty and sufficiently informative. In this report, the upper bound of the estimated parameter error is proposed when the unstructured model uncertainty exists. This upper bound can be used to decide the optimal identification input.