- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Recently Accepted Articles ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Abstract and Applied Analysis
Volume 2014 (2014), Article ID 512037, 9 pages
Event-Based Filter Design for Sensor Networks with Missing Measurements
1Department of Applied Mathematics, Nanjing University of Finance and Economics, Nanjing, Jiangsu 210023, China
2School of Automation, Southeast University, Nanjing 210096, China
3Institute of Information and Control Engineering Technology, Nanjing Normal University, Nanjing, Jiangsu 210042, China
Received 26 December 2013; Accepted 16 January 2014; Published 24 February 2014
Academic Editor: Ming Liu
Copyright © 2014 Jinliang Liu 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.
In order to save network resources and network bandwidth, this paper proposed an event triggered mechanism based on sampled-data information, which has some advantages over existing ones. Considering the missing sensor measurements and the network-induced delay in the transmission, we construct a new event-based filtering by taking the effect of sensor faults with different failure rates. By using the Lyapunov stability theory and the stochastic analysis theory, sufficient criteria are derived for the existence of a solution to the algorithm of the event-based filter design. Finally, an example is exploited to illustrate the effectiveness of the proposed method.
The application of network technologies is becoming increasingly important in many areas for its predominant advantages (such as low cost, simple installation and maintenance, and high reliability). However, it is known that implementing a communication network can induce multiple channel transmission, packet dropout, and so on. This has motivated much attention to the research. Various techniques have been proposed to deal with the above issues, such as time triggered communication scheme [1, 2] and event triggered communication scheme [3–7]. In general, under a time triggered communication scheme, a fixed sampling interval should be selected under worse conditions such as external disturbances and time delay. However, such situation rarely occurs. Hence, time triggered communication scheme can lead to transmit much unnecessary information and inefficient utilization of limited network resources. Comparing with time triggered scheme, the event triggered scheme can save the network resources such as network bandwidth while maintaining the control performance. The adoption of the event triggered scheme has drawn a great deal of interest to the researchers. The authors in  firstly proposed a kind of event triggered scheme which decided whether the newly sampled signal should be transmitted to the controller and invested the controller design problem. In , the authors took the sensor and actuator faults into consideration and studied the reliable control design for networked control system under event triggered scheme. The authors in  were concerned with the control design problem of event triggered networked systems with both state and control input quantization. In , the authors discussed the event-based fault detection for the networked systems with communication delay and nonlinear perturbation.
On the other hand, the filtering problem has been a hot topic over the past decades. A large number of outstanding results have been published [9, 11–18]. For example, the researchers in  studied the problem of event-based filtering for networked systems with communication delay. Most of them are based on an assumption that sensors are working without any flaws. However, the distortion of the sensor usually occurs due to the internal noise or external disturbance. Therefore, it is necessary to discuss the situation when the filter cannot receive the value of the process accurately. Fortunately, much effort has been put into this issue. The authors in  were concerned with reliable filter design for sampled-data systems with probabilistic sensor signal distortion. In , the authors investigated reliable filter design for T-S fuzzy model based networked control systems with random sensor failure.
To the best of our knowledge, the filter design of event triggering network-based systems with random sensor failures is still an open problem, which motivates our present paper. The main contributions of the obtained results are as follows: (I) the insertion of the event triggering generator saves the network resources and network bandwidth. (II) A new kind of event triggering network-based systems with probabilistic sensor failures and network induced delay, which has not been investigated in the existing literatures, is proposed.
This paper is outlined as follows. Section 2 presents the modeling. Section 3 presents our main stability theorem and develops a filter design method. In Section 4, an example is given to illustrate the effectiveness of the proposed method.
and denote the -dimensional Euclidean space and the set of real matrices; the superscript “” stands for matrix transposition; is the identity matrix of appropriate dimension; stands for the Euclidean vector norm or the induced matrix 2-norm as appropriate; the notation (resp., ), for means that the matrix is real symmetric positive definite (resp., positive semidefinite), when is a stochastic variable. For a matrix and two symmetric matrices and , denotes a symmetric matrix, where denotes the entries implied by symmetry.
2. System Description
As shown in Figure 1, our aim in this paper is to investigate an event-based reliable filtering design problem by taking the effect of sensor faults. Suppose the plant model is governed by where is the state vector, is the measured output, is the signal to be estimated, is the process noise belonging to , , , , and are known constant matrices with appropriate dimensions.
Remark 1. Considering the network induced delay, the transmission time of measured output from sensor to filt cannot be neglected. The input of the filter is not , but , in fact, . is the network induced delay, and , where is a positive real number.
For the network-based system in described in Figure 1, we propose the following filter: where is the filter state, is the input of the filter, , , are the filter matrices of appropriate dimensions.
If we take the missing sensor measurements into consideration, (2) can be described as where , () being unrelated random variables, and the mathematical expectation and variance of are and , respectively.
Remark 2. When , it means the sensor works normally. When , it means the sensor completely failed and the signal transmitted by sensor is lost. When , it means the signal at the filter is smaller or greater than it actually is .
In order to reduce the load of network transmission and save the network resources such as network bandwidth, it is necessary to introduce an event triggered mechanism. As is shown in Figure 1, an event generator is constructed between the sensor and filter, which is used to decide whether the measured output should be sent to the filter. We adopt the following judgement algorithm: where is a symmetric positive definite matrix, , , and are unrelated random variables. Only when the expectation of a certain function of current sampled value and the previously transmitted one violate (4), it can be sent out to the filter.
Remark 3. Under the event triggering (4), the release times are assumed to be . Due to the delay in the network transmission, the measured output will arrive at the filter at the instants , respectively.
Based on the above analysis, considering the behavior of ZOH, the input of the filter is
Case 1. When , define a function as Obviously,
Case 2. When , consider the following two intervals:
From , we can deduce that there must exist satisfying
Moreover, and satisfy (4). Set where . Clearly, we have
Define as Then, one can easily get Due to , the third row in (13) holds. Obviously,
It can be deduced from the definition of and the event triggering scheme (4); for , the following inequality holds
Remark 4. From (15), it can be easily obtained that
Employing , the input of the filter can be rewritten as Obviously,
Remark 5. The event triggering scheme (4) can be applied to the situation when the sensor have failures. Besides, the effect of the network environment is also taken into consideration. From the modeling process, we can see that the system (21) is more general.
Before giving the main results in the next section, the following lemmas will be introduced, which will be helpful in deriving the main results.
Lemma 6 (see ). For any vectors , , and positive definite matrix , the following inequality holds:
Lemma 7 (see ). , , and are matrices with appropriate dimensions, ; then if and only if the following two inequalities hold
3. Main Results
In this section, we will invest a new approach to guarantee the filter error system (21) to be globally asymptotically stable. A sufficient condition is established for (21). Then, the explicit filter design method in (20) is given.
Theorem 8. For given scalars , , , , and , under the event triggered communication scheme (4), the augmented system (21) is asymptotically stable with an performance index for the disturbance attention, if there exist positive definite matrices , , and matrices , with appropriate dimensions, such that where
Proof. Set , ; choose the Lyapunov functional candidate
Calculating the difference of along the solution of (27) and taking the mathematical expectation, we obtain where and
Then by employing free weight matrix method [23, 24], we have where .
By Lemma 6, we can easily get
Combine (28)–(31) and (16), we have Subsequently, by the well known Schur complement and Lemma 7, from (25), we can deduce Similar to the method in , the filter error system (21) is asymptotically stable.
Theorem 9. For given parameters , , , and , the filter error system (21) is asymptotically stable with performance level , if there exist positive definite matrices , , , and , , , , , , , , and with appropriate dimensions, such that where The filter parameters are given by
Proof. Since , there exist nonsingular matrix and symmetrical matrix satisfying .
Now premultiply and postmultiply Equation (25) with and , and define new variables as We can obtain (35). Therefore, (35) holds, only if (25) holds. From Theorem 8, the filter error system (21) is asymptotic stable with performance level .
4. Simulation Examples
Consider a specific network controlled system of Equation (21) under a structure: Assume and the failure rates of the sensors are and .
According to Theorem 9, when performance level , the following parameters can be obtained from the solution of (35) and (36) by using the LMI technique: From (38), the corresponding filter parameters can be obtained as and the parameter in the event triggering scheme (4) is .
Suppose the initial condition and external disturbance
Based on the designed filter above, the response of the error and the probabilistic failure are given in Figures 2 and 3, respectively. Figure 4 describes the release instants and release interval. It is easy to see from Figures 2–4 that the filter design method in this paper is effectiveness.
This paper investigates a filter design for a class of network-based systems under an event triggered mechanism. In particular, the system under study is a more general sensor failure model. Considering the uncertain time delay, the uncertain network environment and probabilistic missing sensor measurements, we introduce an event triggered mechanism into the system. By using the free-weighting matrix method and the LMI techniques, the fundamental stability conditions are obtained and the filter design methods are developed. Finally, a numerical example is given to demonstrate the effectiveness of the proposed designed method.
We would like to point out that it is possible to extend our main results to the nonlinear systems such as T-S fuzzy systems, and complex network systems. This will also be one of our future research issues.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
This work is partly supported by the National Natural Science Foundation of China (no. 11226240 and no. 61273115), National Center for International Joint Research on E-Business Information Processing under Grant no. 2013B01035, the Natural Science Foundation of Jiangsu Province of China (nos. BK2012469, BK2012211, and BK2012847), the Natural Science Foundation of the Jiangsu Higher Education Institutions of China (no. 12KJD120001 and no. 13KJB110010), and a Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD).
- H. Yang, Y. Xia, and P. Shi, “Stabilization of networked control systems with nonuniform random sampling periods,” International Journal of Robust and Nonlinear Control, vol. 21, no. 5, pp. 501–526, 2011.
- Q.-L. Han, “A discrete delay decomposition approach to stability of linear retarded and neutral systems,” Automatica, vol. 45, no. 2, pp. 517–524, 2009.
- D. Yue, E. Tian, and Q. Han, “A delay system method for designing event-triggered controllers of networked control systems,” IEEE Transactions on Automatic Control, vol. 58, no. 2, pp. 475–481, 2013.
- C. Peng and T. Yang, “Event-triggered communication and control co-design for networked control systems,” Automatica, vol. 49, no. 5, pp. 1326–1332, 2013.
- A. Molin and S. Hirche, “On the optimality of certainty equivalence for event-triggered control systems,” IEEE Transactions on Automatic Control, vol. 58, no. 2, pp. 470–474, 2013.
- C. Peng, Q. Han, and D. Yue, “To transmit or not to transmit: a discrete event-triggered communication scheme for networked takagisugeno fuzzy systems,” IEEE Transactions on Fuzzy Systems, vol. 21, no. 1, pp. 164–170, 2013.
- X. Yin and D. Yue, “Event-triggered tracking control for heterogeneous multi-agent systems with markov communication delays,” Journal of the Franklin Institute, vol. 350, no. 3, pp. 651–669, 2013.
- J. Liu and D. Yue, “Event-triggering in networked systems with probabilistic sensor and actuator faults,” Information Sciences, vol. 240, no. 10, pp. 145–160, 2013.
- S. Hu and D. Yue, “Event-based filtering for networked system with communication delay,” Signal Processing, vol. 92, no. 9, pp. 2029–2039, 2012.
- J. Liu and D. Yue, “Event-based fault detection for networked systems with communication delay and nonlinear perturbation,” Journal of the Franklin Institute, vol. 350, no. 9, pp. 2791–2807, 2013.
- M. Liu, P. Shi, L. Zhang, and X. Zhao, “Fault-tolerant control for nonlinear markovian jump systems via proportional and derivative sliding mode observer technique,” IEEE Transactions on Circuits and Systems I, vol. 58, no. 11, pp. 2755–2764, 2011.
- M. Liu and P. Shi, “Sensor fault estimation and tolerant control for itô stochastic systems with a descriptor sliding mode approach,” Automatica, vol. 49, no. 5, pp. 1242–1250, 2013.
- P. Shi, X. Luan, and F. Liu, “ filtering for discrete-time systems with stochastic incomplete measurement and mixed delays,” IEEE Transactions on Industrial Electronics, vol. 59, no. 6, pp. 2732–2739, 2012.
- F. Li and X. Zhang, “Delay-range-dependent robust filtering for singular lpv systems with time variant delay,” International Journal of Innovative Computing, Information and Control, vol. 9, no. 1, pp. 339–353, 2013.
- Y. Xu, H. Su, Y. Pan, and Z. Wu, “Robust filtering for networked stochastic systems with randomly occurring sensor nonlinearities and packet dropouts,” Signal Processing, vol. 93, no. 7, pp. 1794–1803, 2013.
- J. Liu, Z. Gu, and S. Hu, “ filtering for Markovian jump systems with time-varying delays,” International Journal of Innovative Computing, Information and Control, vol. 7, no. 3, pp. 1299–1310, 2011.
- C. Peng, D. Yue, E. Tian, and Z. Gu, “Observer-based fault detection for networked control systems with network quality of services,” Applied Mathematical Modelling, vol. 34, no. 6, pp. 1653–1661, 2010.
- C. Peng, Y.-C. Tian, and D. Yue, “Output feedback control of discrete-time systems in networked environments,” IEEE Transactions on Systems, Man, and Cybernetics A, vol. 41, no. 1, pp. 185–190, 2011.
- Z. Gu, E. Tian, and J. Liu, “Reliable filter design for sampled-data systems with consideration of probabilistic sensor signal distortion,” IET Signal Processing, vol. 7, no. 5, pp. 420–426, 2013.
- E. Tian and D. Yue, “Reliable filter design for T-S fuzzy model-based networked control systems with random sensor failure,” International Journal of Robust and Nonlinear Control, vol. 23, no. 1, pp. 15–32, 2013.
- Y. Wang, L. Xie, and C. E. de Souza, “Robust control of a class of uncertain nonlinear systems,” Systems and Control Letters, vol. 19, no. 2, pp. 139–149, 1992.
- D. Yue, E. Tian, Y. Zhang, and C. Peng, “Delay-distribution-dependent stability and stabilization of T-S fuzzy systems with probabilistic interval delay,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 39, no. 2, pp. 503–516, 2009.
- Y. He, M. Wu, J.-H. She, and G.-P. Liu, “Parameter-dependent Lyapunov functional for stability of time-delay systems with polytopic-type uncertainties,” IEEE Transactions on Automatic Control, vol. 49, no. 5, pp. 828–832, 2004.
- D. Yue, Q.-L. Han, and J. Lam, “Network-based robust control of systems with uncertainty,” Automatica, vol. 41, no. 6, pp. 999–1007, 2005.
- X.-M. Zhang and Q.-L. Han, “Delay-dependent robust filtering for uncertain discrete-time systems with time-varying delay based on a finite sum inequality,” IEEE Transactions on Circuits and Systems II, vol. 53, no. 12, pp. 1466–1470, 2006.