Research Article  Open Access
Jianmin Jiao, "DelayDependent Stability Criteria for Singular Systems with Interval TimeVarying Delay", Mathematical Problems in Engineering, vol. 2012, Article ID 570834, 16 pages, 2012. https://doi.org/10.1155/2012/570834
DelayDependent Stability Criteria for Singular Systems with Interval TimeVarying Delay
Abstract
This paper is concerned with stability analysis for singular systems with interval timevarying delay. By constructing a novel Lyapunov functional combined with reciprocally convex approach and linear matrix inequality (LMI) technique, improved delaydependent stability criteria for the considered systems to be regular, impulse free, and stable are established. The developed results have advantages over some previous ones as they involve fewer decision variables yet less conservatism. Numerical examples are provided to demonstrate the effectiveness of the proposed stability results.
1. Introduction
It is well known that time delays frequently occur in many practical systems, such as biological systems, chemical systems, electronic systems, and network control systems. The time delays are regarded as the major source of oscillation, instability, and poor performance of dynamic systems. During the last two decades, there has been some remarkable theoretical and practical progress in stability, stabilization, and robust control of linear timedelay systems [1, 2]. Currently, the results of stability for timedelay systems mainly focus on timevarying delay with range zero to an upper bound. However, in practice, the delay range may have a nonzero lower bound, and such systems are referred to interval timevarying delay systems. Typical examples for interval timedelay systems are networked control systems [3]. With rapid advancement in the networked control systems technology, a number of significant results have been reported in the recent past for the stability of interval timedelay systems [3–14]. For example, in [3], a discretized Lyapunov functional approach is employed to obtain stability criteria for linear uncertain systems with interval timevarying delays. By using freeweighting matrices, [4, 5] present some less conservative stability conditions. The freeweighting matrices method was further improved in [6, 7] by constructing augmented Lyapunov functionals. The freeweighting matrices method is regarded as an effective way to reduce the conservatism of the stability results; however, one chief shortcoming is that too many freeweighting matrices introduced in the theoretical derivation sometimes cannot reduce the conservatism of the obtained results, on the contrary, they make criteria mathematically complex and computationally less effective. In [8, 9], via different Lyapunov functionals with fewer matrix variables whose derivative is estimated using Jensen inequality, some simple stability criteria were obtained, these results were improved in [10] using the convex analysis method, and the result in [10] was further improved in [11] using the reciprocally convex approach. Recently, by introducing some integral terms in the augmented vector and using the Lyapunov functionals with tripleintegral terms, some less conservative results were obtained in [12–14].
Singular systems, which are also referred to as descriptor systems, differential algebraic systems, or semistate systems whose behaviors are described by differential equations (or difference equations) and algebraic equations. Singular systems have strong practical relevance in a variety of physical processes such as power systems, social economic systems, and circuit systems [15]. For this reason, singular systems have attracted a lot of researches from mathematics and control communities. A great number of fundamental results based on the theory of regular systems have been extended to the area of singular systems [16]. Recently, more and more attention has been paid to singular systems with delay. Singular timedelay systems can preserve the structure of practical systems and have extensive applications in various engineering systems, including aircraft attitude control, flexible arm control of robots, largescale electric network control, chemical engineering systems, and lossless transmission lines [17]. It is well known that the stability analysis for singular systems is much more complicated than that for regular systems because it requires to consider not only stability, but also regularity and absence of impulse (for continuous singular systems) [18–28] or causality (for discrete singular systems) [29–32]. In order to obtain stability conditions of singular timedelay systems, many efforts have been made in the literature, among which the model transformation and bounding technique for crossterms are often used [18–20]. However, it is well known that these two kinds of methods are the main source of conservatism. Without using model transformation and bounding technique for crossterms, some improved stability conditions with less conservatism have been provided by introducing freeweighting matrices [21, 22], integral inequality [23, 24], delay decomposition [25], and parameterized Lyapunov functional [26]. However, the involved time delays of [18–26] are all time invariant, which limits the scope of applications of the given results. In the case where timevarying delays appear in singular systems, some stability results were proposed in [27, 28]. The range of timevarying delay considered in [27, 28] is from zero to an upper bound. In the case of the lower bound of delay is not restricted to be zero, the stability criteria in [27, 28] are conservative because they do not take into account the information of the lower bound of delay. Very recently, singular systems with timevarying delay in a range are studied in [33–38]. Nevertheless, there still exists some room for deriving less conservative as well as computationally less expensive stability criteria, which has motivated this paper.
In this paper, we will construct a novel Lyapunov functional and extend the reciprocally convex approach inspired by Park et al. [11] to analyze the stability of singular systems with interval timevarying delay. Some improved results for the considered systems to be regular, impulse free, and stable are established in terms of LMIs. The obtained stability criteria involve fewer decision variables comparable to those based on the freeweighting matrices method; hence they are mathematically less complex and computationally more efficient. Meanwhile, the new criteria are less conservative than existing ones, which will be demonstrated by some numerical examples.
Notations. Throughout this paper, denotes the dimensional Euclidean space, while refers to the set of all real matrices with rows and columns. represents the transpose of the matrix , while denotes the inverse of . For real symmetric matrices and , the notation (resp., ) means matrix is positive semidefinite (resp., positivedefinite). is the identity matrix with appropriate dimensions. refers to the Euclidean norm of the vector , that is, .
2. Problem Formulation and Preliminaries
Consider the singular system with interval timevarying delay described by: where is the state vector, and is a continuous vectorvalued initial function of . The matrix may be singular, and it is assumed that , are known real constant matrices with appropriate dimensions. is the timevarying delay and is assumed to satisfy where and are known constants; and represent the lower and upper bounds of the timevarying , respectively, is the bound on the delay derivative.
The purpose of this paper is to formulate new delaydependent criteria to check the stability of singular timedelay system (2.1). Let us give the following definitions and lemmas, which will play an indispensable role in deriving our criteria.
Definition 2.1 (see [16]). (i) The pair is said to be regular if is not identically zero. (ii) The pair is said to be impulse free if .
Definition 2.2 (see [35]). (i) The singular timedelay system (2.1) is said to be regular and impulse free if the pairs and are regular and impulse free. (ii) The singular timedelay system (2.1) is said to be stable if for any , there exists a scalar such that, for any compatible initial conditions satisfying , the solution of system (2.1) satisfies for any , more over .
Definition 2.3 (see [11]). Let be a given finite number of functions such that they have positive values in an open subset of . Then, a reciprocally convex combination of these functions over is a function of form where the real numbers satisfy and .
Lemma 2.4 (see [1]). For any symmetric positive define matrix , scalars and vector function such that the integrations concerned are well defined, the following inequality holds
Lemma 2.5 (see [11]). Let have positive values in an open subset . Then, the reciprocally convex combination of over satisfies
3. Main Results
In this section, we consider the stability of singular timedelay system (2.1). For simplicity, we define that , are block entry matrices, for example, and . Now, we provide a novel delaydependent stability criterion for singular timedelay system (2.1) as follows.
Theorem 3.1. Given scalars and , for any delay satisfying (2.2), singular timedelay system (2.1) is regular, impulse free, and stable if there exist matrices , , , , , , , , and , such that the following LMIs (3.1)–(3.3) hold where , and is any fullcolumn rank matrix satisfying .
Proof. The proof is divided into two parts. The first part deals with the regularity and impulsefree properties, and the second part treats the stability property of the studied class of systems. First of all, we show that the singular timedelay system (2.1) is regular and impulse free for any timedelay satisfying (2.2). From LMI (3.1), it follows that
where
From LMI (3.4), it easy to see that , using the fact that and , we have
Since , there must exist two invertible matrices such that
Set
where is a nonsingular matrix. Premultiplying and postmultiplying (3.6) by and , respectively, we can easily formulate the following inequality:
where and are not relevant in the following discussion; the real expression of these two matrices are omitted here. From (3.9), it is easy to see that
which implies that matrix is nonsingular. Otherwise, supposing that is singular, there must exist a nonzero vector , which ensures that . And then we can conclude that , and this contradicts (3.10). So is nonsingular, which implies that the pair is regular and impulse free [16].
On the other hand, Premultiplying and postmultiplying (3.4) by and , respectively, yields
From (3.11), taking conditions and into account, we obtain
Proceeding in a similar manner as above, we can find (3.12) implies that the pair is regular and impulse free. Thus, according to Definition 2.2, singular timedelay system (2.1) is regular and impulse free for any timedelay satisfying (2.2).
In the following, we will prove that singular delaydelay system (2.1) is stable. Construct a new class Lyapunov functional for system (2.1) as follows:
where
with
It is easy to see that
The time derivative of each along trajectories of the singular timedelay system (2.1) can be processed as
Applying Lemma 2.4 to the last five integral terms, we can obtain
where
Premultiplying and postmultiplying LMI (3.2) by and , respectively, we have
where
By using Lemma 2.5, we have
Similarly, from LMI (3.3), we can get
Combining (3.20)–(3.25), we can furthermore get
Note that when or , we have or , respectively. So relation (3.26) still holds.
Noting , we have
where is any matrix with appropriate dimensions.
Adding (3.27) to the right of (3.16) and substituting (3.17),(3.18), and (3.26) into (3.16), we have
From LMI (3.1), it is easy to see that for any . Hence, there exists a sufficiently small positive scalar , such that
By (3.29), the following steps are similar to the proof of Proposition 1 in [35] and Theorem 1 in [36], we can deduce that singular timedelay system (2.1) is stable. This completes our proof.
Remark 3.2. Based on the new Lyapunov functional in (3.13), together with the reciprocally convex approach and LMI technique, Theorem 3.1 proposed a delaydependent criterion guaranteeing the considered singular timedelay system to be regular, impulse free, and stable. Lyapunov functional (3.13) is constructed by using the idea of “delaypartitioning” [25, 37, 39]. We consider the lower bound , upper bound , and timevarying delay in our Lyapunov functional by dividing them into two equal segments, such that the information of delayed states , and are all taken into account. Therefore, the criterion in Theorem 3.1 is expected to be less conservative than some previous ones, which will be demonstrated in the sequel.
If the matrix is nonsingular, then the stability problem of singular system (2.1) is reduced to analyzing the stability of the regular system:
This problem has been widely studied in the recent literature (see, e.g., [3–14]). We choose Lyapunov functional:
where
and is defined in (3.13).
By employing the Lyapunov functional (3.31) and using the similar proof of Theorem 3.1, we can obtain the following delaydependent stability criterion for timedelay system (3.30).
Corollary 3.3. Given scalars and , for any delay satisfying (2.2), timedelay system (3.30) is stable if there exist matrices , , , , , , and , , such that LMIs (3.2), (3.3), and following LMI (3.34) hold where matrix is defined in (3.1).
Remark 3.4. As mentioned in the introduction, through the use of freeweighting matrices [5] or the introduction of the Lyapunov functional with tripleintegral terms [13, 14], we can derive less conservative stability criteria for system (3.30), but it makes the criteria mathematically complex and computationally less effective. In this paper, the Lyapunov functional (3.31) does not contain any tripleintegral terms, and when estimating , we have not introduced freeweighting matrix. From a mathematical point of view, it is simple. Meanwhile, Corollary 3.3 in this paper is less conservative than the results in [5, 13, 14], which will be demonstrated in the sequel.
Theorem 3.1 and Corollary 3.3 give new stability criteria of system (2.1) and system (3.30) with satisfying (2.2), respectively. They can be applied to both slow and fast timevarying delays only if is known. In many circumstances, the information of the time derivative of delay is unknown or the time derivative of delay is known but . Regarding this case, the delaydependent and rateindependent criteria can be derived by choosing in Theorem 3.1 and Corollary 3.3, respectively. Therefore, we have the following Corollaries 3.5 and 3.6.
Corollary 3.5. Given scalars , for any delay satisfying , singular system (2.1) is regular, impulse free, and stable if there exist matrices , , and , such that LMIs (3.2), (3.3), and following LMI (3.35) hold where matrices and are defined in (3.1).
Corollary 3.6. Given scalars , for any delay satisfying , timedelay system (3.30) is stable if there exist matrices , and , such that LMIs (3.2), (3.3), and following LMI (3.36) hold where matrix is defined in (3.1).
Remark 3.7. By employing similar method and choosing in Lyapunov functionals (3.13) and (3.31), all the above results obtained in this paper can easily extend to delaydependent stability results for systems (2.1) and (3.30) with . As for example, we can obtain a delaydependent stability criterion using similar method of Theorem 3.1 for system (2.1) with , and is a constant time delay. The obtained stability criterion for this case is shown in the following Corollary 3.8.
Corollary 3.8. Given scalar , for any constant time delay satisfying , singular timedelay system (2.1) is regular, impulse free, and stable if there exist matrices , , , , , , , and , such that LMIs (3.2),(3.3), and following LMI (3.37) hold where , is any fullcolumn rank matrix satisfying , and are block entry matrices, for example, , and .
Remark 3.9. It is worth pointing out that the obtained results in this paper are formulated in terms of LMIs, they can be easily solved using any LMI toolbox like one of Matlab or the one of Scilab.
4. Numerical Examples
In this section, we use three examples and compare our results with the previous ones to show the effectiveness of ours.
Example 4.1. Consider the singular timedelay system (2.1) with
For various , the allowable upper bounds , which guarantee regular, impulse free, and stable of system (2.1) for given lower bounds , are listed in Table 1. From Table 1, it can be seen that the stability criterion in Theorem 3.1 is less conservative than that in [35]. Especially, when , the result in [35] is not feasible while the allowable upper bounds can also be obtained from Theorem 3.1 in this paper.

Example 4.2. Consider the timedelay system (3.30) with
For various , the allowable upper bounds , which guarantee the stability of system (3.30) for given lower bounds , are listed in Table 2. Moreover, the number of decision variables involved in the stability criteria are given in Table 3. From Tables 2 and 3, it can be seen that Corollary 3.3 in this paper has fewer decision variables and less conservatism than those results in [5, 13, 14].

For unknown , the allowable upper bounds , which guarantee the stability of system (3.30) for given lower bounds , are listed in Table 4. From Table 4, it can be seen that Corollary 3.6 in this paper give larger upper bounds of time delay than ones in [4, 5, 8–11, 13, 14].

Example 4.3. Consider the singular timedelay system (2.1) with where is a scalar. It is assumed that and are constant delays satisfying . For various , the allowable upper bounds , which guarantee regular, impulse free, and stable of system (2.1), are listed in Table 5. From Table 5, it is clear that the Corollary 3.8 in this paper gives better results than those in [18, 22–24, 26].
5. Conclusion
This paper deals with the problem of stability for singular systems with interval timevarying delay. A new stability criterion for singular systems to be regular, impulse free, and stable is proposed in terms of LMIs. Based on the obtained criterion, some improved stability results for the regular systems with interval timevarying delay are also given. The obtained results in this paper have been shown to be less conservative than recently reported results. Moreover, the proposed method decreases the computational complexity comparable to some existing methods. Three numerical examples are given to illustrate the applicability of the results.
Acknowledgments
The author is grateful to the academic editor and four anonymous referees whose comments have helped to improve the final version of the present work. This work was supported by the scientific research program funded by Shaanxi provincial education department (nos. 2010JK400 and 11JK0509) and the scientific research project funded by Baoji University of Arts and Sciences (no. 11044).
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Copyright © 2012 Jianmin Jiao. 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.