Controller validation for stability and performance based on an uncertainty region designed from an identified model

This paper focuses on the validation (for stability and for performance) of a controller that has been designed from an unbiased model of the true system, identified either in open-loop or in closed-loop using a prediction error framework. A controller is said to be validated for stability if it stabilizes all models defined by an ellipsoidal parametric uncertainty set containing the true system with some prescribed probability. The same controller is said to be validated for performance if the worst case performance achieved by this controller over the plants in the uncertainty region is better than some threshold value.