Data-based model refinement for linear and hammerstein systems using subspace identification and adaptive disturbance rejection

摘要

First principle models and empirical models are necessarily approximate. In this paper we develop two empirical approaches that use a delta model to modify an initial model by means of cascade, parallel or feedback augmentation. A sub-space based nonlinear identification algorithm and an adaptive disturbance rejection algorithm are both used to construct the delta model. Three classes of errors in the initial model, i.e. unmodeled dynamics, parametric errors and initial condition errors are considered. Some illustrative examples are presented