Model validation for control and controller validation in a prediction error identification framework. I: Theory
[For Part II see ibid. 417-427 (2003; Zbl 1015.93012) reviewed below.] The paper is devoted to the model validation problem. A model validation procedure is proposed that consists of a prediction error identification experiment with a full order model. It delivers a parametric uncertainty ellipsoid and a corresponding set of parameterized transfer functions, which is called the prediction error uncertainty set. Such an uncertainty set differs from the classical uncertainty descriptions used in robust control analysis and design. A robust control analysis theory for such uncertainty sets is developed. It covers two distinct aspects: controller validation and model validation for robust control. Necessary and sufficient conditions for a specific controller are presented. Also, a measure for the size of such a prediction error uncertainty set is presented. It is directly connected to the size of set controllers that stabilize all systems in the model uncertainty set. This allows the authors to establish that one uncertainty set is better tuned for robust control design than another, leading to control-oriented validation objectives.
scientific article; zbMATH DE number 1900981
scientific article