Robust Control, Optimization, and Applications to Markovian Jumping Systems
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DelayDependent Robust Exponential Stability and Analysis for a Class of Uncertain Markovian Jumping System with Multiple Delays
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 delaydependent 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 [1–5]. The stability results for delayed systems can be generally classified into two categories: delayindependent stability criteria and delaydependent criteria. And the delaydependent results are often less conservative than the delayindependent ones, especially when the time delays are small. Therefore, much more attention has been focused on study of the delaydependent stability conditions in recent years. For example, the system transformation method in [6], the descriptor system method in [7], parameterdependent LyapunovKrasovskii functional method in [8], Jensen inequality method in [9], Freeweighting 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 [18–20].
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 [21–27], 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 delayindependent 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 delaydependent 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 Freeweighting 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 timevarying delays is investigated. Some new delaydependent 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 squareintegrable 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 timevarying 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 timevarying delay with , , , for any , where , and are positive scalar constants; is the initial function defined in with ; , , and are matrix functions with timevarying 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 Lebesguemeasurable elements and satisfying
Remark 1. When , the system with multiple timevarying 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 timevarying 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 timevarying 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 LyapunovKrasovskii 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.

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.

5. Conclusion
The robust exponential stability and performance analysis for uncertain Markovian jumping system with multiple timevarying delays has been investigated based on the reciprocally convex approach. Some new delaydependent 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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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.