Approaches in identification and model validation for robust control design

Abstract

Approaches of identification and model validation problem are investigated and for some model structures it is shown how the uncertainty overbounds can be calculated via some convex optimization problems in the form of LMFs. A certain sets of output noise and classes of model structures are considered and their properties are also transformed into LMI conditions. We have also proposed an iterative scheme that uses these approaches for robust control design. Simulation examples are given to show the implementability of these approaches.