[PDF][PDF] Discussion on:" Mean Square Exponential Stability for Some Stochastic Linear Discrete Time Systems"
HJ Chizeck, C Chen, Y Ji…�- European Journal of�…, 2006 - researchgate.net
In the paper by Dragan and Morozan the stability properties of a class of discrete-time linear
timevarying systems with multiplicative noise and inhomogeneous Markov jumping
parameters are investigated. Four definitions of stability in the exponential mean square
sense are introduced. These different stability definitions are not equivalent. The SESMS
property (strongly exponentially stable in mean square) is directly related to the exponential
stability of a corresponding sequence of Lyapunov operators. This definition does not�…
timevarying systems with multiplicative noise and inhomogeneous Markov jumping
parameters are investigated. Four definitions of stability in the exponential mean square
sense are introduced. These different stability definitions are not equivalent. The SESMS
property (strongly exponentially stable in mean square) is directly related to the exponential
stability of a corresponding sequence of Lyapunov operators. This definition does not�…
[CITATION][C] Discussion on:“Mean Square Exponential Stability for Some Stochastic Linear Discrete Time Systems”
OLV Costa�- European Journal of Control, 2006 - Elsevier
An important question is:''Which stability definitions are of most practical significance?''In
particular, which definition (s) are meaningful in engineering applications, and which
definition (s) are easy to test? Consider Example 3.11. A linear time-varying system with
random jumping parameters is driven by a finite-state (stationary/homogeneous) Markov
process x�t � 1� � A0�t, i�x�k� where i 2 f1, 2g denotes the states of Markov chain with the
probability transition matrix p �
particular, which definition (s) are meaningful in engineering applications, and which
definition (s) are easy to test? Consider Example 3.11. A linear time-varying system with
random jumping parameters is driven by a finite-state (stationary/homogeneous) Markov
process x�t � 1� � A0�t, i�x�k� where i 2 f1, 2g denotes the states of Markov chain with the
probability transition matrix p �