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This paper considers a linear continuous- or discrete-time dynamic object in the absence of its mathematical model. As is demonstrated below, a control law that suboptimally damps initial and (or) exogenous disturbances of such objects can be implemented based on experimental and a priori data. The approach involves the methods of robust control design and duality theory as well as the technique of linear matrix inequalities. [ABSTRACT FROM AUTHOR]

For linear plants with unstructured or structured uncertainty of bounded norm, this paper designs Pareto optimal robust controllers in terms of linear matrix inequalities in multicriteria control problems with the generalized H2 or γ0 norms. The controller design procedure is based on optimization of a scalar objective function (Germeier convolution) and semi-definite programming. The developed theory is used to design multicriteria robust controllers in the stabilization problem for a rotor in electromagnetic bearings. [ABSTRACT FROM AUTHOR]

The need for a game-theoretical formulation of the problem of linear-quadratic control under nonmeasurable plant state where the functional depends on the uncertain initial state was substantiated. The minimax output control law in terms of linear matrix inequalities that may be regarded as the optimal robust control law for the given set of the plant initial states was established assuming that the measurable components of the initial state were known and the nonmeasurable components take on values within the given ellipsoid. The results obtained were generalized to the plants with uncertain parameters. [ABSTRACT FROM AUTHOR]

The admissible value interval for the Lagrange multiplier in robust H∞ output control is shown to be finite. Its bounds are determined and a computation procedure is designed to estimate the minimal external perturbation suppression level that can be attained in an uncertain system with unknown bounded parameters by a dynamic output controller. [ABSTRACT FROM AUTHOR]