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This paper presents a new controller validation method for linear multivariable time-invariant models. Classical prediction error system identification methods deliver uncertainty regions which are nonstandard in the robust control literature. Our controller validation criterion computes an upper bound for the worst case performance, measured in terms of(More)
— in this paper, we describe an architecture of a distributed ADPLL (All Digitall Phase Lock Loop) network based on bang-bang phase detectors that are interconnected asymmetrically. It allows an automatic selection between two operating modes (uni-and bidirectional) to avoid mode-locking phenomenon, to accelerate the network convergence and to improve the(More)
Within a stochastic noise framework, the validation of a model yields an ellipsoidal parameter uncertainty set, from which a corresponding uncertainty set can be constructed in the space of transfer functions. We display the role of the experimental conditions used for validation on the shape of this validated set, and we connect a measure of the size of(More)
In this paper, we illustrate our new results on model validation for control and controller validation in a prediction error identification framework, developed in a companion paper (Gevers et al., 2002), through two realistic simulation examples, covering widely different control design applications. The first is the control of a flexible mechanical system(More)