Time invariant stochastic bilinear system

Model description: 

Consider the following time invariant stochastic bilinear system:

$$\begin{align*} \begin{bmatrix}x_1(t+1)\\x_2(t+1)\end{bmatrix} &= \left\{ \begin{bmatrix}0.2 & 0.4 \\ 0.5 & -0.3\end{bmatrix} + \begin{bmatrix}0.3 & 0.2 \\ -0.3 & 0.4\end{bmatrix}\omega(t) \right\} \begin{bmatrix}x_1(t) \\ x_2(t)\end{bmatrix}+ \begin{bmatrix}2 & 5 \\ 3 & 9\end{bmatrix} \begin{bmatrix}x_1(t) \\ x_2(t)\end{bmatrix}u(t) + \begin{bmatrix}-0.3 \\ 0.4\end{bmatrix}u(t), \\ \begin{bmatrix}y_1(t) \\ y_2(t)\end{bmatrix}& = \begin{bmatrix} 0.7 & 0.8 \\ -0.9 & -0.6\end{bmatrix} \begin{bmatrix}x_1(t) \\ x_2(t)\end{bmatrix}, \end{align*}$$

where

$u(t)=0.24[y_1(t) + y_2(t)] + 0.32[y_1(t-1) + y_2(t-1)]$

and $\omega(t)$ is a white noise with zero mean and variance 0.2.

Type: 

Form: 

Model order: 

2

Time domain: 

Linearity: 

Publication details: 

TitleRandom parameter discrete bilinear system stability
Publication TypeConference Paper
Year of Publication1989
AuthorsYang, Xueshan, Mohler R.R., and Chen Lung-Kee
Conference NameProceedings of the 28th IEEE Conference on Decision and Control, 1989.
Date Published12/1989
PublisherIEEE
Conference LocationTampa, FL
Accession Number3685072
Keywordsdiscrete systems, feedback, linear systems, noise, nonlinear systems, stability criteria, stochastic systems
AbstractStability of discrete, time-varying, stochastic, bilinear systems is studied. Bilinear systems with output feedback are included. Mean-square stability conditions are derived for stochastic models without the assumption of stationarity for the random noise. The feedback function includes a larger class of functions than the class of linear functions or functions satisfying the Lipschitz condition. The sufficient stabilizing conditions depend only on the coefficient matrices of the bilinear system
DOI10.1109/CDC.1989.70323