Research Article  Open Access
SampledData Control of Singular Systems with Time Delays
Abstract
This paper is concerned with sampleddata controller design for singular systems with time delay. It is assumed that the sampling periods are arbitrarily varying but bounded. A timedependent Lyapunov function is proposed, which is positive definite at sampling times but not necessarily positive definite inside the sampling intervals. Combining input delay approach with Lyapunov method, sufficient conditions are derived which guarante that the singular system is regular, impulse free, and exponentially stable. Then, the existence conditions of desired sampleddata controller can be obtained, which are formulated in terms of strict linear matrix inequality. Finally, numerical examples are given to demonstrate the effectiveness and the benefits of the proposed method.
1. Introduction
In the last decade, considerable attention has been devoted to sampleddata control systems, because modern control systems usually employ digital technology for controller implementation [1–8]. The systems can adopt a digital computer to sample and quantize a continuoustime measurement signal to produce a discretetime control input signal, which will be converted back into a continuoustime control input signal using a zeroorder hold (ZOH) [9]. Recently, three main approaches have been adopted to analyze the sampleddata systems. The first one is based on discretetime models [9]. The second one is based on the representation of the sampleddata system in the form of impulsive model. The impulsive model approach was applied to sampleddata stabilization of linear uncertain systems in the case of constant sampling, where a piecewise linear in time Lyapunov function was suggested [10]. The third one is the input delay approach [11], where the system is modeled as a continuoustime system with the delayed control input, and it is popular and has been widely adopted in sampleddata systems [11–15]. In [16], a novel time dependent Lyapunov functionalbased technique for sampleddata control has been introduced in the framework of the input delay approach. The most significant advantage of the method is that the sawtooth evolution of the timevarying delay induced by sample and hold is used. Thus, Recently, the timedependent Lyapunov functional method has been applied to all sorts of sampleddata systems, and some useful results have been obtained (see, e.g., [17–24] and the references therein).
On the other hand, singular systems, also referred to as descriptor systems, generalized statespace systems, differentialalgebraic systems, or semistate systems, provide convenient and natural representations in the description of economic systems, power systems, and circuits systems [25–30], and it have been extensively studied in the past few years due to the fact that singular systems better describe physical systems than statespace ones. Apparently, in nowadays digitalized world, it is of both theoretical significance and practical importance to analyze how a digitalized control signal would influence the singular systems. In other words, there is a vital need to investigate the sampleddata control for singular systems. Unfortunately, although sampleddata control technologies have been developed relatively well in control theory, the particular sampleddata control for singular systems has so far received very little attention mainly due to the mathematical complexity. Indeed, the essential difficulties would be how to deal with the obtained results to guarantee the considered singular systems not only to be stable but also to be regular and impulse free in order to ensure the existence, uniqueness, and absence of impulses of a solution to a given system, how to fully adopt the available information about the actual sampling pattern, and how to actually design a set of easytoimplement sampleddata controllers in order to guarantee that the singular systems are exponentially stable. It is, therefore, the main aim of this paper to challenge the sampleddata control for singular systems by overcoming the aforementioned three major difficulties.
This paper is concerned with the sampleddata control of singular systems with time delays which are important sources of oscillation, divergence, and instability in systems, and thus timedelay systems have been widely studied recently [31, 32]. In terms of LMI approach, stability conditions are proposed to guarantee the considered system to be regular, impulse free, and exponentially stable. In order to make full use of the available information about the actual sampling pattern, a timedependent Lyapunov functional is proposed. The positive definitiveness of the given Lyapunov functional is required only at sampling times but not necessarily inside the sampling intervals. Two numerical examples are given to illustrate the effectiveness of the methods given in the paper.
Notation. Throughout this paper, the superscripts “” and “−1” stand for the transpose of a matrix and the inverse of a matrix. denotes the dimensional Euclidean space, and is the set of real matrices. The notation (), where and are symmetric matrices, means that is positive definite (positive semidefinite). is the identity matrix of appropriate dimensions. denotes the Euclidean norm of a vector and its induced norm of a matrix. and refer to the maximal and minimal eigenvalues of the matrix , respectively. For a symmetric matrix, “*” denotes the matrix entries implied by symmetry.
2. Problem Formulation
Consider the following sampleddata control of singular system: where is the state vector, is the control input, and the initial condition, , is a continuous vector valued initial function of . , , and are known matrices of appropriate dimensions, where may be singular, and we assume that rank . is a given time delay.
In this paper, it is assumed that we only have the measurement at the sampling instant ; that is, only discrete measurements of are available for control purposes, and the control signal is assumed to be generated by using a zeroorderhold (ZOH) function with a sequence of hold times:
Also, the sampling is not required to be periodic, and the only assumption is that the distance between any two consecutive sampling instants is less than a given bound. It is assumed that for all , where represents the upper bound of the sampling periods. Then, for system (1), we consider a statefeedback control law of the form where is the local gain matrix of the state feedback controller to be determined.
By substituting (4) into (1), we obtain Throughout this paper, we will use the following concepts.
Definition 1. (1)The pair (, ) is said to be regular if is not identically zero.(2)The pair (, ) is said to be impulse free if .
Definition 2 (see [33]). (1)The sampleddata control of singular system (5) is said to be regular and impulse free if the pair (, ) is regular and impulse free.(2)The sampleddata control of singular system (5) is said to be exponentially stable, if there exist scalars and such that where .(3)The sampleddata control of singular system (5) is said to be exponentially admissible, if it is regular, impulse free, and exponentially stable.
Lemma 3. Given singular system (5), the following inequality holds: where , .
Proof. For any , it follows from (5) that
Applying the CauchySchwarz inequality, we find from (8) that
Using the CauchySchwarz inequality again, we obtain from (9) that
Applying the GronwallBellman Lemma to (10), we can obtain (7) immediately. This completes the proof.
3. Main Results
In this section, the exponential stability of sampleddata control for singular system (5) is first investigated based on the timedependent Lyapunov functional approach, and sufficient condition is derived to guarantee the system stability and synthesize the sampleddata controllers in the form of (4).
Theorem 4. Given scale , the sampleddata control for singular system (5) is exponentially stable if there exist symmetric positivedefinite matrices , , , , , , , , , , , such thatwhere
Proof. Since rank , there exist nonsingular matrices and such that
Similar to (15), we define
From (11), we have , and .
Premultiplying and postmultiplying by and , respectively, we have , which implies that is nonsingular and the pair is regular and impulse free. Then, by Definition 2, the sampleddata control for singular system (5) is regular and impulse free.
Next, we will show the exponential stability of system (5). Consider the following Lyapunov functional of sampleddata control for singular system (5):
It is noted that, similar to [16], we have
Therefore, is continuous in time since . Calculating the time derivative of along the trajectories of (5) gives the following result:
According to Jensen integral inequality [34], we have
Applying (26) and (27) to (22) and (24), respectively, we can get
Furthermore, based on Schur complement, it can be found that for any appropriately dimensioned matrix
which implies
where
From (31), we can get
Applying the above inequality to (23), we obtain
On the other hand, according to (5), for any appropriately dimensioned matrix , , the following equation holds:
Then, adding the righthand side of (35) to , we obtain from (20), (21), (25), (28), (29), and (34) that for where is given in (38), and
It is noted that
From (12) and (13), we can find that
Based on Schur complement, we have from (13)
From (40), (41), we can get that
Thus, we obtain from (36), (41), and (43) that
Thus, it follows that, for ,
Based on Lemma 3 and (45) and letting , we can conclude that for
It can be calculated that
where
Based on (46) and (47), we can conclude that
Thus, according to Definition 2, the sampleddata control for singular system (5) is exponentially admissible. This completes the proof.
Remark 5. It is noted that based on the timedependent Lyapunov functional method, three ()dependent terms , , and are introduced in the Lyapunov functional, which make good use of the available information about the actual sampling pattern. As a consequence, the proposed result has less conservatism.
Remark 6. It should be pointed out that if ( is a sufficiently small positive scalar) and = = = = 0, then + + reduces to where which was first proposed for linear sampleddata systems in [16]. On the other hand, in [16] the Lyapunov functional should be positive definite at the whole sampling intervals. While the Lyapunov functional (17) is positive definite only at sampling times but not necessarily positive definite inside the sampling intervals. Thus, the Lyapunov functional used in this paper is more general and desirable than the one adopted in [16].
Based on Theorem 4, we can obtain the following corollary.
Corollary 7. If (12) and (13) are feasible for , then the system (5) is exponentially stable with a small enough decay rate.
Now, we will design the sampleddata controller (4) such that system (5) is exponentially stable. Based on Theorem 4, the sampleddata controller design method for system (5) is provided in the following theorem.
Theorem 8. Given scalars and , the sampleddata control for singular system (5) is exponentially stabile if there exist symmetric positivedefinite matrices , , , , , , , , , , such thatwhere , , , , , , , , , , , , , are as those in Theorem 4, and
Then singular system (5) is exponentially stable. Furthermore, the sampleddata controller gain matrix in (5) is given by
Proof. Letting , and , we can get from (12)(13) that (52)(53) hold. This completes the proof.
Remark 9. It should be mentioned that the problem of sampleddata exponential stability of singular systems with time constant delays and uncertain sampling is solved in Theorem 8, and sufficient conditions of the existence of the desired sampleddata controllers are also given, which are formulated by LMIs and can readily be solved by standard numerical software.
Based on Theorem 8, we can obtain the following corollary.
Corollary 10. If (12), (13) and (52), (53) are feasible for , then system (5) is exponentially stable with a small enough decay rate, and the desired state feedback controller gains are given in (55).
4. Numerical Examples
In this section, two illustrative examples will be provided to demonstrate the validity and reduced conservatism of the proposed approaches.
Example 1. Consider the singular system with sampleddata control in (5). The system parameters are described as follows:
In this example, we choose , .
Applying Theorem 4, as shown in Table 1, we can obtain the different maximum values of the upper bound for different . From Table 1, we can find the influence of the choice of on the value of the upper bound . To be specific, a larger value of corresponds to a smaller value of the upper bound .

Next, we will design the sampleddata controller (4) such that system (5) is exponentially stable. Choosing and , and using the MATLAB LMI Toolbox to solve the LMIs (12) and (13), we can get the following gain matrix in
That is, there exists a sampleddata controller such that system (5) is exponentially stable for any sampling period ≤ 0.2390.
For the case of constant sampling period, based on Theorem 1 of [35], the maximum sampling period is 0.0158. While based on Corollary 10 with , the largest sampling period ensuring the stability of system (9) is 0.0389, which is 146.2% larger than that of [35]. Thus, our proposed approach is able to achieve less conservative results and essentially improves the existing one.
Example 2. Consider the singular system with sampleddata control in (5) with the following parameters:
In this example, we choose .
For different timedelay , the influence of the choice of the upper bound on the value of can be seen in Figure 1. From Figure 1, it is clear that when timedelay is fixed, for a larger upper bound , the value of is usually smaller, and when is fixed, for a larger , the value of is usually larger.
Using Theorem 8 with given in this paper, the maximum value of the upper bound that system (5) is exponentially stable is 0.3029, and the corresponding gain matrix is
Under the above gain matrix, the response curves of system (5) are exhibited in Figure 2, which shows that the states are tending to zero; that is, singular system (5) can be stabilized by the proposed sampleddata controller.
5. Conclusion
In this paper, a sampleddata control approach was proposed for the singular systems with time delays. A timedependent Lyapunov functional was introduced for the systems, which was positive definite at sampling times but not necessarily positive definite inside the sampling intervals. By the usage of the Lyapunov approach, sufficient condition was proposed to ensure the exponential stability of the singular systems, which can significantly reduce the conservatism. The available information about the actual sampling pattern was fully used. Based on the stability criterion, the desired sampleddata controller has also been designed. Finally, two illustrative examples have been presented to show the effectiveness and potential of the proposed new design techniques.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
Acknowledgments
This work was supported by the National Natural Science Foundation of China (51109090), The key project of Fujian Provincial Department of Science & Technology (2012H0030), the university’s innovative project of Xiamen Science & Technology Bureau (3502Z20123019), and the project of New Century Excellent Talents of Colleges and Universities of Fujian Province (JA12181).
References
 P. Shi, “Filtering on sampleddata systems with parametric uncertainty,” IEEE Transactions on Automatic Control, vol. 43, no. 7, pp. 1022–1027, 1998. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 S. K. Nguang and P. Shi, “Fuzzy ${H}_{\infty}$ output feedback control of nonlinear systems under sampled measurements,” Automatica, vol. 39, no. 12, pp. 2169–2174, 2003. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 E. Fridman, A. Seuret, and J.P. Richard, “Robust sampleddata stabilization of linear systems: an input delay approach,” Automatica, vol. 40, no. 8, pp. 1441–1446, 2004. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 L. Hu, P. Shi, and P. M. Frank, “Robust sampleddata control for Markovian jump linear systems,” Automatica, vol. 42, no. 11, pp. 2025–2030, 2006. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 H. Gao, J. Wu, and P. Shi, “Robust sampleddata ${H}_{\infty}$ control with stochastic sampling,” Automatica, vol. 45, no. 7, pp. 1729–1736, 2009. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 Q. Liu, R. Wang, and D. Wu, “Stability analysis for sampleddata systems based on multiple lyapunov functional method,” International Journal of Innovative Computing Information and Control, vol. 8, no. 9, pp. 6345–6355, 2012. View at: Google Scholar
 I. Mizumoto, Y. Fujimoto, N. Watanabe, and Z. Iwai, “Fast rate adaptive output feedback control of multirate sampled systems with an adaptive output estimator,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 7, pp. 4377–4394, 2011. View at: Google Scholar
 A. Seuret, “A novel stability analysis of linear systems under asynchronous samplings,” Automatica, vol. 48, no. 1, pp. 177–182, 2012. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 W. Zhang, M. S. Branicky, and S. M. Phillips, “Stability of networked control systems,” IEEE Control Systems Magazine, vol. 21, no. 1, pp. 84–97, 2001. View at: Publisher Site  Google Scholar
 L. Hu, J. Lam, Y. Cao, and H. Shao, “A linear matrix inequality (LMI) approach to robust H2 sampleddata control for linear uncertain systems,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 33, no. 1, pp. 149–155, 2003. View at: Publisher Site  Google Scholar
 E. Fridman, A. Seuret, and J.P. Richard, “Robust sampleddata stabilization of linear systems: an input delay approach,” Automatica, vol. 40, no. 8, pp. 1441–1446, 2004. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 Z. Wu, P. Shi, H. Su, and J. Chu, “Stochastic synchronization of markovian jump neural networks with timevarying delay using sampled data,” IEEE Transactions on Cybernetics, vol. 43, no. 6, pp. 1796–1806, 2013. View at: Google Scholar
 B. Shen, Z. Wang, and X. Liu, “A stochastic sampleddata approach to distributed ${H}_{\infty}$ filtering in sensor networks,” IEEE Transactions on Circuits and Systems, vol. 58, no. 9, pp. 2237–2246, 2011. View at: Publisher Site  Google Scholar  MathSciNet
 B. Shen, Z. Wang, and X. Liu, “Sampleddata synchronization control of dynamical networks with stochastic sampling,” IEEE Transactions on Automatic Control, vol. 57, no. 10, pp. 2644–2650, 2012. View at: Publisher Site  Google Scholar  MathSciNet
 J. Li, J. Wu, S. Wang, and J. Cui, “Stability and stabilization for sampleddata systems with probabilistic sampling,” International Journal of Innovative Computing, Information and Control, vol. 6, no. 7, pp. 3299–3311, 2010. View at: Google Scholar
 E. Fridman, “A refined input delay approach to sampleddata control,” Automatica, vol. 46, no. 2, pp. 421–427, 2010. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 E. Fridman and A. Blighovsky, “Robust sampleddata control of a class of semilinear parabolic systems,” Automatica, vol. 48, no. 5, pp. 826–836, 2012. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 Z. Wu, P. Shi, H. Su, and J. Chu, “Exponential synchronization of neural networks with discrete and distributed delays under timevarying sampling,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 9, pp. 1368–1376, 2012. View at: Publisher Site  Google Scholar
 Z. Wu, P. Shi, H. Su, and J. Chu, “Networkbased robust passive control for fuzzy systems with randomly occurring uncertainties,” IEEE Transactions on Fuzzy Systems, vol. 21, no. 5, pp. 966–971, 2012. View at: Google Scholar
 K. Liu, E. Fridman, and L. Hetel, “Stability and ${L}_{2}$gain analysis of networked control systems under RoundRobin scheduling: a timedelay approach,” Systems & Control Letters, vol. 61, no. 5, pp. 666–675, 2012. View at: Publisher Site  Google Scholar  MathSciNet
 K. Liu and E. Fridman, “Wirtinger's inequality and Lyapunovbased sampleddata stabilization,” Automatica, vol. 48, no. 1, pp. 102–108, 2012. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 A. Seuret, “A novel stability analysis of linear systems under asynchronous samplings,” Automatica, vol. 48, no. 1, pp. 177–182, 2012. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 A. Seuret, “Stability analysis for sampleddata systems with a timevarying period,” in Proceedings of the 48th IEEE Conference on Decision and Control held jointly with 2009 28th Chinese Control Conference (CDC/CCC '09), pp. 8130–8135, December 2009. View at: Publisher Site  Google Scholar
 W. Jiang and A. Seuret, “Improved stability analysis of networked control systems under asynchronous sampling and input delay,” in Proceedings of the 2nd IFAC Workshop on Distributed Estimation and Control in Networked Systems (NecSys '10), pp. 79–84, September 2010. View at: Publisher Site  Google Scholar
 Z.G. Wu, J. H. Park, H. Su, and J. Chu, “Stochastic stability analysis for discretetime singular Markov jump systems with timevarying delay and piecewiseconstant transition probabilities,” Journal of the Franklin Institute, vol. 349, no. 9, pp. 2889–2902, 2012. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
 P. Liu, “Further results on the exponential stability criteria for time delay singular systems with delaydependence,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 6, pp. 4015–4024, 2012. View at: Google Scholar
 P. Liu, “Further results on the exponential stability criteria for time delay singular systems with delaydependence,” International Journal of Innovative Computing, Information and Control, vol. 8, no. 6, pp. 4015–4024, 2012. View at: Google Scholar
 H. Wanga, B. Zhoua, R. Lub, and A. Xueb, “New stability and stabilization criteria for a class of fuzzy singular systems with timevarying delay,” Journal of the Franklin Institute, 2013. View at: Google Scholar
 Y. Wang, Q. Wang, P. Zhou, and D. Duan, “Robust guaranteed cost control for singular Markovian jump systems with timevarying delay,” ISA Transactions, vol. 51, no. 5, pp. 559–565, 2012. View at: Publisher Site  Google Scholar
 Y. Wang, P. Shi, Q. Wang, and D. Duan, “Exponential ${H}_{\infty}$ filtering for singular Markovian jump systems with mixed modedependent timevarying delay,” IEEE Transactions on Circuits and Systems, vol. 60, no. 9, pp. 2440–2452, 2013. View at: Publisher Site  Google Scholar  MathSciNet
 R. Loxton, K. L. Teo, and V. Rehbock, “An optimization approach to statedelay identification,” IEEE Transactions on Automatic Control, vol. 55, no. 9, pp. 2113–2119, 2010. View at: Publisher Site  Google Scholar
 Q. Chai, R. Loxton, K. L. Teo, and C. Yang, “A unified parameter identification method for nonlinear timedelay systems,” Journal of Industrial and Management Optimization, vol. 9, no. 2, pp. 471–486, 2013. View at: Google Scholar
 Z. Wu, H. Su, and J. Chu, “Delaydependent ${H}_{\infty}$ control for singular Markovian jump systems with time delay,” Optimal Control Applications & Methods, vol. 30, no. 5, pp. 443–461, 2009. View at: Publisher Site  Google Scholar  MathSciNet
 K. Gu, V. L. Kharitonov, and J. Chen, Stability of TimeDelay Systems, Birkhäuser, Boston, Mass, USA, 2003. View at: Publisher Site  MathSciNet
 H. K. Lam and F. H. F. Leung, “Stabilization of chaotic systems using linear sampleddata controller,” International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, vol. 17, no. 6, pp. 2021–2031, 2007. View at: Publisher Site  Google Scholar  Zentralblatt MATH  MathSciNet
Copyright
Copyright © 2014 Zheng Minjie et al. 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.