Existence of a Mean-Square Stabilizing Solution to a Modified Algebraic Riccati Equation
Date of Issue2018
School of Electrical and Electronic Engineering
In this paper, the existence of a mean-square stabilizing solution to a discrete-time modified algebraic Riccati equation (MARE), which arises in the study of some stochastic linear quadratic optimal control problems, is investigated. The theory of cone-invariant operators is employed as the mathematical tool to tackle this problem. We provide some criteria for the cone stability, cone observability, and cone detectability of a class of cone-invariant systems in terms of the associated distinguished eigenvalues. Then two scenarios concerning the problem of existence in terms of the positive definiteness of the input weighting matrix are considered. We first study the MARE under the assumption that the input weighting matrix in the cost function is positive definite. Then an explicit necessary and sufficient condition is obtained. Such a condition is derived for the very first time and it indicates that the common condition of observability or detectability of certain stochastic systems is unnecessary. Only the observability of the distinguished eigenvalue at 1 of an associated cone-invariant operator is required. Hence this necessary and sufficient condition is compatible with the one ensuring the existence of a stabilizing solution to the standard algebraic Riccati equation. However, when the input weighting matrix is only positive semidefinite, this condition does not hold. In this case, we get a sufficient condition and a necessary condition, respectively. These two conditions coincide when the input weighting matrix is indeed positive definite.
Modified Algebraic Riccati Equation
SIAM Journal on Control and Optimization
© 2018 Society for Industrial and Applied Mathematics. This paper was published in SIAM Journal on Control and Optimization and is made available as an electronic reprint (preprint) with permission of Society for Industrial and Applied Mathematics. The published version is available at: [http://dx.doi.org/10.1137/16M1098255]. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper is prohibited and is subject to penalties under law.