The purpose of this paper is to provide a parametric description (parametrization) of all static output feedback stabilizing controllers for linear stochastic discrete‐time systems with Markovian switching, applications of this result to simultaneous and robust stabilization problems and obtaining of algorithms for computing stabilizing gains.
The proposed approach presents parameterization in terms of coupled linear matrix equations and quadratic matrix inequalities which depend on parameter matrices similar to weight matrices in linear quadratic regulator (LQR) theory. To avoid implementation problems, a convex approximation technique is used and linear matrix inequalities (LMI)‐based algorithms are obtained for computing of stabilizing gain.
The algorithms obtained in this paper are non‐iterative and used computationally efficient LMI technique. Moreover, it is possible to use well‐known LQR methodology in the process of controller design.
As a result of this paper, a new unified approach to design of static output feedback stabilizing control is developed. This approach leads to efficient stabilizing gain computation algorithms for both stochastic systems with Markovian switching and deterministic systems with polytopic uncertainty.
Pakshin, P. and Soloviev, S. (2009), "Parametrization of static output feedback controllers for Markovian switching systems and related robust control problems", Kybernetes, Vol. 38 No. 7/8, pp. 1106-1120. https://doi.org/10.1108/03684920910976853Download as .RIS
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