Controller validation for stability and performance based on a frequency domain uncertainty region obtained by stochastic embedding (original) (raw)
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We propose a model validation procedure that consists of a prediction error identification experiment with a full order model. It delivers a parametric uncertainty ellipsoid and a corresponding set of parametrized transfer functions, which we call PE (for Prediction Error) uncertainty set. Such uncertainty set differs from the classical uncertainty descriptions used in robust control analysis and design. We develop a robust control analysis theory for such uncertainty sets, which covers two distinct aspects. (1) Controller validation. We present necessary and sufficient conditions for a specific controller to stabilize -or to achieve a given level of performance with -all systems in such PE uncertainty set. (2) Model validation for robust control. We present a measure for the size of such PE uncertainty set that is directly connected to the size of a set controllers that stabilize all systems in the model uncertainty set. This allows us to establish that one uncertainty set is better tuned for robust control design than another, leading to control-oriented validation objectives.