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The Scientific World Journal
Volume 2014 (2014), Article ID 560234, 11 pages
http://dx.doi.org/10.1155/2014/560234
Research Article

Robust Filtering for a Class of Complex Networks with Stochastic Packet Dropouts and Time Delays

1School of Automation, Nanjing University of Science & Technology, Nanjing 210094, China
2Information Overall Department, North Information Control Group Co., Ltd., Nanjing 211153, China
3Department of Engineering, Faculty of Engineering and Science, University of Agder, 4898 Grimstad, Norway

Received 27 December 2013; Accepted 6 March 2014; Published 27 March 2014

Academic Editors: J. Bajo and Z. Wu

Copyright © 2014 Jie Zhang 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.

Abstract

The robust filtering problem is investigated for a class of complex network systems which has stochastic packet dropouts and time delays, combined with disturbance inputs. The packet dropout phenomenon occurs in a random way and the occurrence probability for each measurement output node is governed by an individual random variable. Besides, the time delay phenomenon is assumed to occur in a nonlinear vector-valued function. We aim to design a filter such that the estimation error converges to zero exponentially in the mean square, while the disturbance rejection attenuation is constrained to a given level by means of the performance index. By constructing the proper Lyapunov-Krasovskii functional, we acquire sufficient conditions to guarantee the stability of the state detection observer for the discrete systems, and the observer gain is also derived by solving linear matrix inequalities. Finally, an illustrative example is provided to show the usefulness and effectiveness of the proposed design method.

1. Introduction

Over the past decades, the filtering problem has drawn particular attention, since filters are insensitive to the exact knowledge of the statistics of the noise signals. Up to now, a great deal of effort has been devoted to the design issues of various kinds of filters, for example, the Kalman filters [13] and filters [49].

In real-world applications, the measurements may contain missing measurements (or incomplete observations) due to various reasons such as high maneuverability of the tracked targets, sensor temporal failures or network congestion. In the past few years, the filtering problem with missing measurements has received much attention [1017]. In [10], a model of multiple missing measurements has been presented by using a diagonal matrix to account for the different missing probabilities for individual sensors. The finite-horizon robust filtering problem has been considered in [11] for discrete-time stochastic systems with probabilistic missing measurements subject to norm-bounded parameter uncertainties. A Markovian jumping process has been employed in [12] to reflect the measurement missing problem. Moreover, the optimal filter design problem has been tackled in [13] for systems with multiple packet dropouts by solving a recursive difference equation (RDE).

On the other hand, the complex networks have been gaining increasing research attention from all fields of the basic science and the technological practice. They have applications in many real-world systems such as the Internet, World Wide Web, food webs, electric power grids, cellular and metabolic networks, scientific citation networks, and social networks [1825]. Due to randomly occurring incomplete phenomenon which occurs in the signal transfer within complex networks, there may be time delays and packet dropouts [2633]. For instance, over a finite horizon, the synchronization and state estimation problems for an array of coupled discrete time-varying stochastic complex networks have been studied based on the recursive linear matrix inequalities (RLMIs) approach [26]. In [29], one of the first few attempts has been made to address the synchronization problem for stochastic discrete-time complex networks with time delays. Furthermore, in [31], a new array of coupled delayed complex networks with stochastic nonlinearities, multiple stochastic disturbances, and mixed time delays in the discrete-time domain has been investigated, and the synchronization stability criteria have been derived by utilizing a novel matrix functional, the properties of the Kronecker product, the free-weighting matrix method, and the stochastic techniques.

Summarizing the above discussion, it should be pointed out that, up to now, the general filter results for complex networks with randomly occurring incomplete information have been very few, especially when the networks exhibit both stochastic natures and disturbance inputs. In this paper, we make an attempt to investigate the problems of the robust filtering for a class of complex systems with stochastic packet dropouts, time delays, and disturbance inputs. By constructing the proper Lyapunov-Krasovskii functional, we can get sufficient conditions, such that the filter error is exponentially stable in mean-square sense, and acquire gain of the designed observer.

The rest of the paper is organized as follows. In Section 2, the problem of complex networks is formulated and some useful lemmas are introduced. In Section 3, some sufficient conditions are established to make sure the robustly exponential stability of the filtering error dynamics. Besides, the gain of observer is also designed by LMI. An illustrated example is given in Section 4 to demonstrate the effectiveness of the proposed method. Finally, we give our conclusions in Section 5.

Notation. The notation used here is fairly standard except where otherwise stated. and denote, respectively, the dimensional Euclidean space and the set of all real matrices. denotes the identity matrix of compatible dimension. The notation (resp., ), where and are symmetric matrices, means that is positive semidefinite (resp., positive definite). represents the transpose of . and denote the maximum and minimum eigenvalue of , respectively. Sym denotes the symmetric matrix . stands for the expectation of the stochastic variable . describes the Euclidean norm of a vector . stands for a block-diagonal matrix whose diagonal blocks are given by . The symbol in a matrix means that the corresponding term of the matrix can be obtained by symmetric property. The symbol denotes the Kronecker product. In symmetric block matrices, the symbol is used as an ellipsis for terms induced by symmetry.

2. Problem Formulation

Consider the following discrete-time complex system with time delays and disturbance: where is the state vector of the th node, is the output of the th node, denotes time-varying delay, and are nonlinear vector-valued functions satisfying certain conditions given later, is the disturbance input belonging to , is a zero mean Gaussian white noise sequence, and is the continuous function quantifying the noise intensity. is the matrix linking the th state variable if , and is the coupled configuration matrix of the network with    but not all zero. As usual, the coupling configuration matrix is symmetric (i.e., ) and satisfies , and are constant matrices with appropriate dimensions, and is a given initial condition sequence.

For the system shown in (1), we make the following assumptions throughout the paper.

Assumption 1. The variable is a scalar Wiener process (Brownian motion) satisfying

Assumption 2. The variable denotes the time-varying delay satisfying where and are constant positive integers representing the lower and upper bounds on the communication delay, respectively.

Assumption 3. and are the nonlinear disturbance which satisfies the following sector-bounded conditions: for all , where , , , and are real matrices of appropriate dimensions and .

Assumption 4. The continuous function satisfies where is known constant scalars.

In this paper, we assume that an unreliable network medium is present between the physical plant and the state detection filter, and this means that the output data is subject to randomly missing phenomenon. The signal received by the state detection filter can be described by where is the measurement output of the th node and is the disturbance input which belongs to . and are constant matrices with appropriate dimensions. is the Bernoulli distributed white sequences governed by where is known constant.

In this paper, we are interested in obtaining , the estimate of the signal , from the actual measured output . We adopt the following filter to be considered for node : where is the estimate of the state , is the estimate of the output , and is the estimator gain matrix to be designed.

Let the estimation error be . By using the Kronecker product, the filtering error system can be obtained from (1), (7), and (9) as follows: where Setting , we subsequently obtain an augmented system as follows: where

Definition 5 (see [34]). The filtering error system (12) is said to be exponentially stable in the mean square if, in case of , for any initial conditions, there exist constants and such that where , for all .

Our aim in this paper is to develop techniques to deal with the robust filtering problem for a class of complex systems with stochastic packet dropouts, time delays, and disturbance inputs. The augmented observer system (12) satisfies the following requirements (Q1) and (Q2), simultaneously:(Q1)the filter error system (12) with is exponentially stable in the mean square;(Q2)under the zero initial condition, the filtering error satisfies

for all nonzero , where is a given disturbance attenuation level.

Lemma 6 (the Schur complement). Given constant matrices ,  and  , where and , then if and only if

3. Main Results

In this part, we will construct the Lyapunov-Krasovskii functional and the use of linear matrix inequality to propose sufficient conditions such that the system error model in (12) could be exponentially stable in mean square. Let us first consider the robust exponential stability analysis problem for the filter error system (12) with .

Theorem 7. Consider the system (1) and suppose that the estimator parameters are given. The system augmented error model (12) with is said to be exponentially stable in mean square, if there exist positive definite matrices    and positive scalars    satisfying the following inequality: where

Proof. Choose the following Lyapunov functional for system (12): where
Then, along the trajectory of system (12) with , we have Next, it can be derived that
Letting the combination of (21) and (22) results in where
Notice that (5) implies
From (26), it follows that
According to Theorem 7, we have ; there must exist a sufficiently small scalar such that Then, it is easy to see from (27) and (28) that the following inequality holds:
According to the definition of , we can derive that where and .
For any scalar , together with (19), the above inequality implies that with and .
In addition, for any integer , summing up both sides of (31) from to with respect to , we have
Due to , So, we can obtain from (32) and (33) the following: with .
Let and . It is obvious from (19) that Meanwhile, we can find easily from (30) that It can be verified that there exists a scalar such that Therefore, from (34)–(37), it is clear to see that
The augmented system (12) with is exponentially mean-square stable according to Definition 5. The proof is complete.

Next, we will analyze the performance of the filtering error system (12).

Theorem 8. Consider the system (1) and suppose that the estimator parameters are given. The system augmented error model (12) is said to be exponentially stable in mean square and satisfies the performance constraint (15) for all nonzero and under the zero initial condition, if there exist positive definite matrices    and positive scalars    satisfying the following inequality: where and other parameters are defined as in Theorem 7.

Proof. It is clear that (39) implies (17). According to Theorem 7, the filtering error system (12) with is robustly exponentially stable in the mean square.
Let us now deal with the performance of the system (15). Construct the same Lyapunov-Krasovskii functional as in Theorem 7. A similar calculation as in the proof of Theorem 7 leads to where and are defined previously.
Setting , inequality (41) can be rewritten as where .
In order to deal with the performance of the filtering system (12), we introduce the following index: where is nonnegative integer.
Under the zero initial condition, one has
According to Theorem 8, we have . Letting , we obtain and the proof is now complete.

We aim at solving the filter design problem for complex network (1). Therefore, we are in a position to consider the filter design problem for the complex network (1). The following theorem provides sufficient conditions for the existence of such filters for system (12). The following result can be easily accessible from Theorem 8, and the proof is therefore omitted.

Theorem 9. Consider the system (1) and suppose that the disturbance attenuation level is given. The system augmented error model (12) is said to be exponentially stable in mean square and satisfies the performance constraint (15) for all nonzero and under the zero initial condition, if there exist positive definite matrices   , matrices , and positive scalars    satisfying the following inequality: where and other parameters are defined as in Theorem 7. Moreover, if the above inequality is feasible, the desired state estimator gains can be determined by

4. Numerical Simulations

In this section, we present an illustrative example to demonstrate the effectiveness of the proposed theorems. Considering the system model (1) with three sensors, the system data are given as follows:

Then, it is easy to see that the constraint (26) can be met with

Let the disturbance attenuation level be . Assume that the initial values are generated that obey uniform distribution over , and , and the delay parameters are chosen as and .

By applying Theorem 9 with help from MATLAB, we can obtain the desired filter parameters as follows:

Then, according to (49), the desired estimator parameters can be designed as

Simulation results are shown in Figures 1, 2, 3, and 4, where Figures 13 plot the missing measurements and ideal measurements for sensors 1–3, respectively, and Figure 4 depicts the output errors. From those figures, we can confirm the superiority of the designed filter.

560234.fig.001
Figure 1: The ideal measurements and the missing measurements of .
560234.fig.002
Figure 2: The ideal measurements and the missing measurements of .
560234.fig.003
Figure 3: The ideal measurements and the missing measurements of .
560234.fig.004
Figure 4: The estimator errors .

5. Conclusions

In this paper, we have studied the robust filtering problem for a class of complex systems with stochastic packet dropouts, time delays, and disturbance inputs. The discrete-time system under study involves multiplicative noises, time-varying delays, sector-bounded nonlinearities, and stochastic packet dropouts. By means of LMIs, sufficient conditions for the robustly exponential stability of the filtering error dynamics have been obtained and, at the same time, the prescribed disturbance rejection attenuation level has been guaranteed. Then, the explicit expression of the desired filter parameters has been derived. A numerical example has been provided to show the usefulness and effectiveness of the proposed design method.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work has been supported by the National Natural Science Foundation of China (Grant no. 61104109), the Natural Science Foundation of Jiangsu Province of China (Grant no. BK2011703), the Support of Science and Technology and Independent Innovation Foundation of Jiangsu Province of China (Grant no. BE2012178), the NUST Outstanding Scholar Supporting Program, and the Doctoral Fund of Ministry of Education of China (Grant no. 20113219110027).

References

  1. J. Hu, Z. Wang, H. Gao, and L. K. Stergioulas, “Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements,” Automatica, vol. 48, no. 9, pp. 2007–2015, 2012. View at Google Scholar
  2. Z. Wang, X. Liu, Y. Liu, J. Liang, and V. Vinciotti, “An extended kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series,” IEEE/ACM Transactions on Computational Biology and Bioinformatics, vol. 6, no. 3, pp. 410–419, 2009. View at Publisher · View at Google Scholar · View at Scopus
  3. F. Yang, Z. Wang, and Y. S. Hung, “Robust Kalman filtering for discrete time-varying uncertain systems with multiplicative noises,” IEEE Transactions on Automatic Control, vol. 47, no. 7, pp. 1179–1183, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. L. Wu, P. Shi, H. Gao, and C. Wang, “H∞ filtering for 2D Markovian jump systems,” Automatica, vol. 44, no. 7, pp. 1849–1858, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. L. Wu and D. W. C. Ho, “Fuzzy filter design for Itô stochastic systems with application to sensor fault detection,” IEEE Transactions on Fuzzy Systems, vol. 17, no. 1, pp. 233–242, 2009. View at Publisher · View at Google Scholar · View at Scopus
  6. X. Su, P. Shi, L. Wu, and Y. Song, “A novel approach to filter design for T-S fuzzy discretetime systems with time-varying delay,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 6, pp. 1114–1129, 2012. View at Google Scholar
  7. X. Su, L. Wu, and P. Shi, “Senor networks with random link failures: distributed filtering for T-S fuzzy systems,” IEEE Transactions on Industrial Informatics, vol. 9, no. 3, pp. 1739–1750, 2013. View at Google Scholar
  8. X. Su, P. Shi, L. Wu, and S. K. Nguang, “Induced l2 filtering of fuzzy stochastic systems with time-varying delays,” IEEE Transactions on Cybernetics, vol. 43, no. 4, pp. 1251–1264, 2013. View at Google Scholar
  9. X. Yao, L. Wu, and W. X. Zheng, “Fault detection filter design for Markovian jump singular systems with intermittent measurements,” IEEE Transactions on Signal Processing, vol. 59, no. 7, pp. 3099–3109, 2011. View at Publisher · View at Google Scholar · View at Scopus
  10. G. Wei, Z. Wang, and H. Shu, “Robust filtering with stochastic nonlinearities and multiple missing measurements,” Automatica, vol. 45, no. 3, pp. 836–841, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. Z. Wang, F. Yang, D. W. C. Ho, and X. Liu, “Robust finite-horizon filtering for stochastic systems with missing measurements,” IEEE Transactions on Signal Processing, vol. 12, no. 6, pp. 137–440, 2005. View at Google Scholar
  12. P. Shi, M. Mahmoud, S. K. Nguang, and A. Ismail, “Robust filtering for jumping systems with mode-dependent delays,” Signal Processing, vol. 86, no. 1, pp. 140–152, 2006. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Sun, L. Xie, and W. Xiao, “Optimal full-order and reduced-order estimators for discrete-time systems with multiple packet dropouts,” IEEE Transactions on Signal Processing, vol. 56, no. 8, pp. 4031–4038, 2008. View at Publisher · View at Google Scholar · View at Scopus
  14. H. Dong, Z. Wang, and H. Gao, “Distributed H filtering for a class of Markovian jump nonlinear time-delay systems over lossy sensor networks,” IEEE Transactions on Industrial Electronics, vol. 60, no. 10, pp. 4665–4672, 2013. View at Google Scholar
  15. H. Dong, Z. Wang, and H. Gao, “Distributed filtering for a class of time varying systems over sensor networks with quantization errors and successive packet dropouts,” IEEE Transactions on Signal Processing, vol. 60, no. 6, pp. 3164–3173, 2012. View at Google Scholar
  16. H. Dong, Z. Wang, D. W. C. Ho, and H. Gao, “Robust H∞ filtering for Markovian jump systems with randomly occurring nonlinearities and sensor saturation: the finite-horizon case,” IEEE Transactions on Signal Processing, vol. 59, no. 7, pp. 3048–3057, 2011. View at Publisher · View at Google Scholar · View at Scopus
  17. Z. Wang, H. Dong, B. Shen, and H. Gao, “Finite-horizon H filtering with missing measurements and quantization effects,” IEEE Transactions on Automatic Control, vol. 58, no. 7, pp. 1707–1718, 2013. View at Google Scholar
  18. B. Shen, Z. Wang, and X. Liu, “Sampled-data synchronization control of complex dynamical networks with stochastic sampling,” IEEE Transactions on Automatic Control, vol. 57, no. 10, pp. 2644–2650, 2012. View at Google Scholar
  19. Y. Tang, Z. Wang, and J.-A. Fang, “Pinning control of fractional-order weighted complex networks,” Chaos, vol. 19, no. 1, Article ID 013112, 2009. View at Publisher · View at Google Scholar · View at Scopus
  20. X. Li, X. Wang, and G. Chen, “Pinning a complex dynamical network to its equilibrium,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 51, no. 10, pp. 2074–2087, 2004. View at Publisher · View at Google Scholar · View at Scopus
  21. C. Lin, Z. Wang, and F. Yang, “Observer-based networked control for continuous-time systems with random sensor delays,” Automatica, vol. 45, no. 2, pp. 578–584, 2009. View at Publisher · View at Google Scholar · View at Scopus
  22. Y. Liu, Z. Wang, J. Liang, and X. Liu, “Synchronization and state estimation for discrete-time complex networks with distributed delays,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 38, no. 5, pp. 1314–1325, 2008. View at Publisher · View at Google Scholar · View at Scopus
  23. Q. Song and J. Cao, “On pinning synchronization of directed and undirected complex dynamical networks,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 57, no. 3, pp. 672–680, 2010. View at Publisher · View at Google Scholar · View at Scopus
  24. L. Wu, Z. Feng, and W. X. Zheng, “Exponential stability analysis for delayed neural networks with switching parameters: average dwell time approach,” IEEE Transactions on Neural Networks, vol. 21, no. 9, pp. 1396–1407, 2010. View at Publisher · View at Google Scholar · View at Scopus
  25. L. Wu, Z. Feng, and J. Lam, “Stability and synchronization of discrete-time neural networks with switching parameters and time-varying delays,” IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 12, pp. 1957–1972, 2013. View at Google Scholar
  26. B. Shen, Z. Wang, and X. Liu, “Bounded H synchronization and state estimation for discrete time-varying stochastic complex networks over a finite horizon,” IEEE Transactions on Neural Networks, vol. 22, no. 1, pp. 145–157, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. B. Shen, Z. Wang, D. Ding, and H. Shu, “H state estimation for complex networks with uncertain inner coupling and incomplete measurements,” IEEE Transactions on Neural Networks and Learning Systems, vol. 24, no. 12, pp. 2027–2037, 2013. View at Google Scholar
  28. D. Ding, Z. Wang, B. Shen, and H. Shu, “H state estimation for discrete-time complex networks with randomly occurring sensor saturations and randomly varying sensor delays,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 5, pp. 725–736, 2012. View at Google Scholar
  29. Y. Liu, Z. Wang, J. Liang, and X. Liu, “Synchronization and state estimation for discrete-time complex networks with distributed delays,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 38, no. 5, pp. 1314–1325, 2008. View at Publisher · View at Google Scholar · View at Scopus
  30. Y. Wang, Z. Wang, and J. Liang, “Global synchronization for delayed complex networks with randomly occurring nonlinearities and multiple stochastic disturbances,” Journal of Physics A: Mathematical and Theoretical, vol. 42, no. 13, Article ID 135101, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. Z. Wang, Y. Wang, and Y. Liu, “Global synchronization for discrete-time stochastic complex networks with randomly occurred nonlinearities and mixed time delays,” IEEE Transactions on Neural Networks, vol. 21, no. 1, pp. 11–25, 2010. View at Publisher · View at Google Scholar · View at Scopus
  32. Y. Liu, Z. Wang, and X. Liu, “Exponential synchronization of complex networks with Markovian jump and mixed delays,” Physics Letters A: General, Atomic and Solid State Physics, vol. 372, no. 22, pp. 3986–3998, 2008. View at Publisher · View at Google Scholar · View at Scopus
  33. Y. Wang, Z. Wang, and J. Liang, “A delay fractioning approach to global synchronization of delayed complex networks with stochastic disturbances,” Physics Letters A: General, Atomic and Solid State Physics, vol. 372, no. 39, pp. 6066–6073, 2008. View at Publisher · View at Google Scholar · View at Scopus
  34. Z. Wang, D. W. C. Ho, Y. Liu, and X. Liu, “Robust H control for a class of nonlinear discrete time-delay stochastic systems with missing measurements,” Automatica, vol. 45, no. 3, pp. 684–691, 2009. View at Publisher · View at Google Scholar · View at Scopus