Research Article  Open Access
Boren Li, "DelayDependent Stability Criteria for Systems with Interval TimeVarying Delay", Journal of Control Science and Engineering, vol. 2011, Article ID 938749, 9 pages, 2011. https://doi.org/10.1155/2011/938749
DelayDependent Stability Criteria for Systems with Interval TimeVarying Delay
Abstract
This paper is concerned with robust stability of uncertain linear systems with interval timevarying delay. The timevarying delay is assumed to belong to an interval, which means that the derivative of the timevarying delay has an upper bound or a restriction. On other occasions, if we do not take restriction on the derivative of the timevarying delay into consideration, it allows the delay to be a fast timevarying function. The uncertainty under consideration includes a polytopictype uncertainty and a linear fractional normbounded uncertainty. In order to obtain much less conservative results, a new LyapunovKrasovskii functional, which makes use of the information of both the lower and upper bounds of the interval timevarying delay, is proposed to derive some new stability criteria. Numerical examples are given to demonstrate the effectiveness of the proposed stability criteria.
1. Introduction
Time delays are frequently encountered in many fields of science and engineering, including communication network, manufacturing systems, biology, economy, and other areas. During the last two decades, the problem of stability of linear timedelay systems has been the subject of considerable research efforts. Many significant results have been reported in the literature. For the recent progress, the reader is referred to Gu et al. [1] and the references therein.
With the development of networked control technology, increasing attention has been paid to the study of stability analysis and controller design of networked control systems (NCSs) due to their low cost, simple installation and maintenance, and high reliability. For the NCSs, the sampling data and controller signals are transmitted through a network. As a result, it leads to a networkinduced delay in a networked control closedloop system. The existence of such kind of delay in a networkbased control loop may induce instability or poor performance of NCSs. As pointed out by Yue et al. [2], NCSs are typical systems with interval timevarying delay. In fact, we consider the following system controlled through a network where is the state vector, and is the input vector. In the presence of the control network, which is shown in Figure 1, data transfer between the controller and the remote system, for example, sensors and actuators in a distributed control system will induce network delay in addition to the controller proceeding delay. First, since there exists the communication delay between the sensor and the controller and computational delay in the controller, which is shown in Figure 1, the following control law is employed for the system (1) where is the sampling period, is the set of nonnegative integers, and is the controller gain to be determined. Second, substituting (2) into (1) yields the closedloop system by considering the communication delay between the controller and the actuator where the timedelay denotes the time from the instant when sensor nodes sample sensor data from a plant to the instant when actuators transfer data to the plant. Since , defining , , rewrite (3) as where is piecewise linear with derivative for , and is discontinuous at the points . It is clear that for , for all .
Let and . Then we have
The system (3) is equivalent to the linear system (4) with interval timevarying delay described by (5). It should be pointed out that is essential for NCSs.
To cover the routine case of , we consider as a uniformly continuous timevarying function satisfying .
Throughout this paper, we will analyze the following two scenarios of the timevarying delay .
Case 1. is a differentiable function satisfying
Case 2. is a continuous function satisfying where and are the lower and upper delay bounds, respectively, and and are constants.
Remark 1. When , the interval delay becomes routine delay. When is zero, that is, , the timevarying delay becomes constant delay, Case 1 is a special case of the Case 2. We may obtain a less conservative result using Case 1 than that using Case 2. However, if the timevarying delay is not differentiable, or information about derivative of timevarying delay is absent, only Case 2 can be used to handle the situation.
Notation 1. denotes the dimensional Euclidean space, is the set of real matrices, is the identity matrix of appropriate dimensions, the notation (resp., ), for means that the matrix is real positive definite (resp., positive semidefinite). For an arbitrary matrix and two symmetric matrices and , denotes a symmetric matrix, where denotes a block matrix entry implied by symmetry.
2. System Descriptions and Preliminaries
Let in (4), we have the following linear system with interval timevarying delay where and are the constant matrices with appropriate dimensions, is the initial condition of the system.
In this paper, we will study stability criteria of system described by (8) satisfying (6) or (7), employing the following new LyapunovKrasovskii functional: where , , , , , , of appropriate dimensions, and , , , , are positive integer numbers of divisions of intervals and , respectively, and , are the length of each division. When and , we assume that and , respectively. Moreover, we will consider a polytopic uncertainty and a linear fractional normbounded uncertainty which includes a routine normbounded uncertainty as a special case. Some numerical examples will be given to show the improvement over some previous results.
For any delay satisfying Case 1 or Case 2, our objective of this study is to develop new stability criteria which guarantee that system (8) is asymptotically stable and the system (8) subject to some uncertainties is robustly stable. For this purpose, the following lemmas are introduced.
Lemma 1 (see [3]). For any constant matrix , scalar , and vector function such that the following integration is well defined, then it holds that
Applying the Lemma 1 yields the following new integral inequality for crossproduct term.
Lemma 2 (see [4]). For any constant matrix , scalars and vector function such that the following integration is well defined, then it holds that where
3. New Stability Criteria
We first consider asymptotic stability for the nominal system (8). Employing LyapunovKrasovskii functional (9), we have the following result.
Theorem 1. For some given scalars and , the nominal system (8) satisfying (6) is asymptotically stable if there exit real symmetric matrices , , , , , , , and any matrix of appropriate dimensions such that the following LMI holds: where
Proof. Taking the time derivative of with respect to along the trajectory of (8) yields
Applying the Lemma 1, the following inequalities hold:
Applying the Lemma 2, the following inequality holds:
For any matrix of appropriate dimensions, the following equality holds:
where is defined in the top of next page,
Considering (16)–(19) together, we have . If (13) is satisfied, then for some scalar , from which we conclude that the nominal system (8) is asymptotically stable. This completes the proof.
When the restriction on the derivative of the interval timevarying delay is removed, that is, choosing in Theorem 1, we can obtain a delay variety rateindependent criterion for a delay that only satisfies (7).
Corollary 1. For some given scalars , the nominal system (8) satisfying (7) is asymptotically stable, if there exit real symmetric matrices , , , , , and any matrix of appropriate dimensions such that the LMI (13) holds with .
Remark 2. For system (8) with the routine delay case described by and , that is, , the corresponding LyapunovKrasovskii functional reduces to
And for system (8) with the constant delay case described by , that is, , the corresponding LyapunovKrasovskii functional reduces to
Similar to the proof of the Theorem 1, one can easily derive a less conservative results than some exiting ones, respectively, which will be shown through numerical examples in the next section. For the sake of simplicity, the results are omitted.
In what follows, we consider robust stability of the system (8) satisfying (6) or (7) subject to a polytopic uncertainty and a linear fractional normbounded uncertainty. For the polytopic uncertainty, that is, the matrices and in (8) can be expressed as where . Based on Theorem 1, we can easily obtain the following result.
Theorem 2. For some given scalars and , the system described by (8) satisfying (6) subject to polytopic uncertainty (23) is robustly stable, if there exit real symmetric matrices ,, , , and any matrix of appropriate dimensions such that the following LMIs hold for : where is defined in Theorem 1, and
Proof. Construct the following parameterdependent LyapunovKrasovskii functional: where , , , , , , are unknown matrices of appropriate dimensions. Then the proof follows a linear similar to the proof of Theorem 1, we can obtain the following inequality: where is defined in the formula (19), then for some scalar , from which we conclude that the system described by (8) satisfying (6) subject to polytopic uncertainty (23) is robustly stable. This completes the proof.
Similar to Corollary 1, we can easily obtain a delay variety rateindependent criterion for a delay that only satisfies (7).
Corollary 2. For some given scalars , the system described by (8) satisfying (7) subject to polytopic uncertainty (23) is robustly stable if there exit real symmetric matrices , , , , , , and any matrix of appropriate dimensions such that the LMIs (24) hold with for .
Remark 3. We succeed to separate the system matrices and Lyapunov matrices in Theorem 1, so we can easily use parameterdependent LyapunovKrasovskii functional method. Different Lyapunov matrices are used in Theorem 2 for different LMIs, which are distinguished with Proposition 7 in Jiang and Han [6], in which used fixed Lyapunov matrices for different LMIs. In fact parameterdependent LyapunovKrasovskii functional method can reduce stability criteria conservatism significantly. Numerical example will be given to show the improvement with Jiang and Han [6] in the next section.
Next we address the linear fractional normbounded uncertainty. Suppose that matrices and have parameter perturbations as and , which are in the form of where , and are known real constant matrices of appropriate dimensions, and ; is an unknown matrix function with Lesbesgue measurable elements satisfying .
For system (8) with uncertainty (29), we can establish the following result by considering Theorem 1 and applying procedure [9].
Theorem 3. For some given scalars and , the system described by (8) satisfying (6)subject to the linear fractional normbounded uncertainty (29) is robustly stable if there exit real symmetric matrices , , , , , , , any matrix of appropriate dimensions, and a scalar such that the following LMI holds: where , is defined in the formula (13), and is defined in Theorem 1.
Proof. Replacing and in (13) with and , respectively, and multiply both sides of the resulting matrix by vectors for . Next, define . Then, we have the following condition for the admissible uncertainty : where . It is easy to know that and can be rewritten as and , since , it is obvious that the following inequality holds: Applying procedure, both inequalities (31) and (32) are true if and only if there is a , promising that the following condition holds: By Schur complement, for any , (33) is equivalent to (30). This completes the proof.
Similar to Corollary 1, we can easily obtain a delay variety rateindependent criterion for a delay that only satisfies (7).
Corollary 3. For some given scalars , the system described by (8) satisfying (7) subject to the linear fractional normbounded uncertainty (29) is robustly stable if there exit real symmetric matrices , , , , , , any matrix of appropriate dimensions, and a scalar such that the LMI (30) holds with .
Remark 4. It is clear to see that if we set , the linear fractional normbounded uncertainty reduces to the routine normbounded uncertainty, and we can derive a corresponding results for the routine normbounded uncertainty from Theorem 3 and Corollary 3. For the sake of simplicity, the results are omitted.
4. Numerical Examples
In this section, two examples are given to show the effectiveness of the results derived in this paper.
Example 1. Consider the following timedelay system: Table 1 lists the maximum allowable upper bound (MAUB) of the timevarying delay for different , and those in He et al. [5], Jiang and Han [6], and Shao [7].(1)If , by using Corollary 1, we have for and any integer , which is larger than in derived in He et al. [5] and close to in derived in Jiang and Han [6].(2)If and . By using Corollary 1 for , our results listed in the second last column is same as the results derived in Jiang and Han [6]. Moreover, by using Corollary 1 for , we have the results listed in the last column, which are larger than the results derived in He et al. [5] and Jiang and Han(2008).(3)By using Proposition 8 in Jiang and Han [6], we can have for , not have .(4)For the constant delay , that is, . By using Corollary 1, we have for ; 5.7175 for ; 5.9677 for ; 6.0983 for ; 6.1537 for , which is close to analytical delay limit for stability 6.17258.
Example 2. Consider the following timedelay system: where , , , , and suppose that is continuous function. We handle the uncertainty as polytopic uncertainty and normbounded uncertainty, respectively. First, the uncertainty is handled as polytopic uncertainty. Then(1)if is a constant, applying Corollary 2 in this paper yields the result for , which is less conservative than the result given in Fridman and Shaked [10], the result given in Han et al. [11] and the result given in Jiang and Han [6]; (2)if , we have , which is larger than in derived in Fridman and Shaked [10], in derived in Jiang and Han [8] and in derived in Jiang and Han [6], respectively;(3)if , Table 2 lists the maximum allowable upper bounds of the timevarying delay for different lower bounds the delay by Corollary 2. From Table 2, we can see that our results are larger than the previous ones.

Next, the uncertainty is handled as normbounded uncertainty, and we choose and as
Table 3 lists the maximum allowable upper bound (MAUB) of the timevarying delay for different , and those in Jiang and Han [6, 8].(1)If , we have for and any integer , which is larger than in derived in He et.al [5] and same as in derived in Jiang and Han [6].(2)If and , By using Corollary 3, our results listed in the second last column for and in the last column for , which is larger than those in derived in Jiang and Han [6].(3)If is a constant, this is , we have for ; 1.4810 for .
It is clear that proposed stability criteria in this paper can significantly improve some exiting results in the literature.
Remark 5. From Tables 2 and 3, we can find that the conservatism using the criterion derived for the polytopic uncertainty is less than using the criterion derived for normbounded uncertainty, which is distinguished to the result derived in Jiang and Han [6], and there are some reverse results, for there are some fixed Lyapunov matrices in Jiang and Han [6] for different LMIs. Moreover, the conservatism can be reduced for by increasing divisions of the intervals or in this paper.
5. Conclusion
This note presents some new stability criteria for the interval timevarying delay system with a polytopic uncertainty and a linear fractional normbounded uncertainty. We have proposed a new LyapunovKrasovskii functional, which is based on dividing intervals and into and divisions, respectively, to derive some new stability criteria. Numerical examples show that our criteria would be less conservative along with increased and demonstrate our criteria are less conservative than previous ones. Furthermore, based on timedelay system stability results, some future research can be focused on designing feedback controller, which can promise system robust stabilization and satisfy some system performance.
Acknowledgments
This work is supported by NSFC Project 11071087, NSFCGuangdong Joint Foundation Key Project U0735003, and Guangdong Province Natural Science Foundation of China Project 06105413 and DGUTNSYF Project 2010ZQ23.
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Copyright
Copyright © 2011 Boren Li. 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.