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Abstract and Applied Analysis
Volume 2014 (2014), Article ID 738318, 10 pages
http://dx.doi.org/10.1155/2014/738318
Research Article

Delay-Dependent Robust Exponential Stability and Analysis for a Class of Uncertain Markovian Jumping System with Multiple Delays

School of Mathematics Science, Liaocheng University, Shandong 252000, China

Received 26 November 2013; Accepted 23 January 2014; Published 17 March 2014

Academic Editor: Hao Shen

Copyright © 2014 Jianwei Xia. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This paper deals with the problem of robust exponential stability and performance analysis for a class of uncertain Markovian jumping system with multiple delays. Based on the reciprocally convex approach, some novel delay-dependent stability criteria for the addressed system are derived. At last, numerical examples is given presented to show the effectiveness of the proposed results.

1. Introduction

It is well known that time delay is usually the main reason for instability and poor performance of many practical control systems [15]. The stability results for delayed systems can be generally classified into two categories: delay-independent stability criteria and delay-dependent criteria. And the delay-dependent results are often less conservative than the delay-independent ones, especially when the time delays are small. Therefore, much more attention has been focused on study of the delay-dependent stability conditions in recent years. For example, the system transformation method in [6], the descriptor system method in [7], parameter-dependent Lyapunov-Krasovskii functional method in [8], Jensen inequality method in [9], Free-weighting matrix method in [10, 11], integral inequality method in [12], augmented Lyapunov functional method in [13], convex domain method in [14], interval partition method in [15, 16], reciprocally convex method in [17], and so forth. And those approaches have been widely used in the stability analysis for lots of delayed systems in recent years [1820].

On the other hand, since Markovian jumping systems can model many types of dynamic systems subject to abrupt changes in their structures, such as failure prone manufacturing systems, power systems, and economics systems [2127], a great deal of results related to stability analysis and synthesis for this class of systems with time delays has been reported in recent years. For example, for the delay-independent results, sufficient conditions for mean squares to stochastic stability were obtained in [28], while exponential stability conditions were proposed in [29]. The robust filtering problem was dealt with in [30]. For the delay-dependent ones, the stability and control results were presented by resorting to some bounding techniques for some cross terms and using model transformation to the original delay system in [31]. The control and Filtering problem were taken into account in [32] using the Free-weighting matrix method. The stability and analysis was proposed in [33] with the idea of delay partition. Filtering problem with a new index was considered in [34] using the reciprocally convex method. It is worth mentioned that inspite of the deep study for the delayed stochastic in recent years as mentioned above, there are few papers that consider the problem of stability analysis for uncertain stochastic systems with multiple delays, which motivates our study.

In this paper, the robust exponential stability and performance analysis for a class of uncertain Markovian system with multiple time-varying delays is investigated. Some new delay-dependent stability conditions are derived. And numerical simulation is given to demonstrate the effectiveness of the result.

Notation. Throughout this paper, for symmetric matrices and , the notation (resp., ) means that the matrix is positive semidefinite (resp., positive definite); is the identity matrix with appropriate dimension; represents the transpose of the matrix ; denotes the expectation operator with respect to some probability measure ; is the space of square-integrable vector functions over ; refers to the Euclidean vector norm; stands for the usual norm; ) is a probability space with the sample space and is the -algebra of subsets of the sample space. Matrices, if not explicitly mentioned, are assumed to have compatible dimensions.

2. System Description and Preliminaries

Consider the following uncertain Markovian jumping system with multiple time-varying delays: where is the state; is the noise disturbance which is assumed to be an arbitrary signal in ; is the signal to be estimated; is a homogenous stationary Markov chain defined on a complete probability space and taking values in a finite set with generator given by where and , is the transition rate from to if and . The scalar is the time-varying delay with , , , for any , where , and are positive scalar constants; is the initial function defined in with ; , , and are matrix functions with time-varying uncertainties described as , , where are known constant matrices, while uncertainties , are assumed to be norm bounded as where , , , and , in (2) are known constant matrices with appropriate dimensions. The unknown matrix functions are having Lebesgue-measurable elements and satisfying

Remark 1. When , the system with multiple time-varying delays (1)–(3) is actually deduced to the uncertain Markovian jumping system with interval delay, which have been deeply studied in recent years. That is, the obtained results of multiple delayed systems can be directly deduced to the interval delayed systems.

Throughout this paper, we will use the following Definitions and Lemmas.

Definition 2. The uncertain Markovian jumping system with multiple time-varying delays (1)–(3) is said to be robustly exponentially stable in mean square for all admissible uncertainties, if there exist scalars and such that for all , where is the trivial solution of systems (1)–(3) with .

Definition 3. Given a scalar , uncertain Markovian jumping system with multiple time-varying delays (1)–(3) is said to be robustly exponentially stable with a prescribed performance level if it is robustly exponentially stable, and under the zero initial condition, satisfies for all admissible uncertainties and nonzero , where

Lemma 4 (see [9]). For any constant matrix ,  , scalar , vector function such that the integrations in the following are well defined; then

Lemma 5 (see [35]). Let , , be real constant matrices with appropriate dimensions; matrix satisfies . For any , such that ,

Lemma 6 (see [36]). Consider system (1) with , , for any matrices and satisfying ; the following inequality holds where , and

3. Main Results

For simplicity, we define

3.1. Robust Exponential Stability Analysis

The criteria of the robust exponential stability for the systems (1)–(3) are proposed in the following Theorem.

Theorem 7. Systems (1)–(3) with is robustly exponentially stable if there exist positive matrices , , , , , , any matrices with appropriate dimensions satisfying ,  ;  ,  , and positive scalars , such that the following LMI holds where And are defined in (3).

Proof. On one hand, using Lemma 5 and Schur complement lemma to (15), we have
On the other hand, define a new process , . Choose a Lyapunov-Krasovskii functional where
Let be the weak infinitesimal generator of the random process . Then for each ,  , we have
Applying Lemma 4 to results in and applying Lemma 6 to , we have that there exists with , , such that where and are defined in (13). Meanwhile, we note that that is,
Then, we can deduce from (19)–(24) that where is defined in (17). Therefore, by Definition 2 and the results in [37], we have that the system (1) is robustly stable. Now, we will prove the robust stochastic exponential stability in mean square for system (1). Setting , we have Choose , where ; then Integrating the above inequality (27), we get
From (19), it can be inferred that Note that We denote ; then
From (29) to (31), we obtain where
By the similar method, we have where . Therefore, by (28)–(34), we get
Choose such that then
Since , it can be shown from (37) that where which implies that system (10) is robustly exponentially stable by Definition 2. This completes the proof.

3.2. Robust Exponential Stability Analysis

The criteria of the robust exponential stability with performance for the systems (1)–(3) are proposed in the following Theorem.

Theorem 8. Given a scalar , the systems (1)–(3) are robustly exponentially stable with a prescribed performance level if there exist matrices , , , , , , any matrices with appropriate dimensions satisfying , ; ,  , and positive scalars , such that the following LMI holds where

Proof. Implying Lemma 5 and Schur complement lemma to (40), we obtain
Set Then, it is easy to have where is defined in (18). Similar to the proof of Theorem 7, we can obtain where is given in (42) and is defined in (14). Then, it follows from (40) and (45) that This implies that for any nonzero , Therefore, by Definition 3, the system is robustly exponentially stable with a prescribed performance level . This completes the proof.

4. Numerical Example

In this section, we provide an example to demonstrate the effectiveness of the proposed method.

Let and ; consider the systems (1)–(3) with parameters as follows.Mode 1 Mode 2 And , , , , , . Then, by solving the LMI (15) with the constraints in Theorem 7, we obtain

If we fix the lower bound of and , that is, and , for the different , we can get the upper bounds of as in Table 1.

tab1
Table 1: The upper bound of for different .

If we fix the lower bound of and , that is, and , for the different , we can get the upper bounds of as in Table 2.

tab2
Table 2: The upper bound of for different .

5. Conclusion

The robust exponential stability and performance analysis for uncertain Markovian jumping system with multiple time-varying delays has been investigated based on the reciprocally convex approach. Some new delay-dependent stability conditions are obtained in term of LMIs. Numerical example has been proposed to illustrate the effectiveness of result.

Conflict of Interests

The author declares that there is no conflict of interests regarding the publication of this paper.

Acknowledgment

This work was supported National Natural Science Foundation of China under Grant nos. 61004046, 61104117, and 61304064.

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