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Convex formulations are derived for the minimization of uncertainty bounds with respect to a nominal model and given input-output data for general uncertainty models of LFT type. The known data give rise to data-matching conditions that have to be satisfied. It is shown how these conditions, which originally are in the form of BMIs for a number of uncertainty models, can be transformed to LMIs, thus...
This paper proposes an LMI-based design algorithm for saturating output feedback controllers that are robust with respect to parametric uncertainties in the system model. The algorithm is based on the determination of a feasible solution by solving a conservative relaxation of the problem. This feasible solution is then optimized using a procedure known as path following, which sequentially optimizes...
This paper deals with the stabilization problems for a class of discrete-time nonlinear systems. The systems are composed of a linear constant part perturbated by an additive nonlinear function which satisfies a quadratic constraint. A new approach to design a H2 static output feedback controller is developed. It consists on a sufficient condition, formulated as an LMI optimization convex problem...
A new approach for robust fixed-order H∞ controller design by convex optimization is proposed. Linear time-invariant single-input single-output systems represented by a finite set of complex values in the frequency domain are considered. It is shown that the H∞ robust performance condition can be approximated by a set of linear or convex constraints with respect to the parameters of a linearly parameterized...
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