Distributed state estimation for uncertain Markov-type sensor networks with mode-dependent distributed delays
SUMMARY In this paper, the distributed state estimation problem is investigated for a class of sensor networks described by uncertain discrete‐time dynamical systems with Markovian jumping parameters and distributed time‐delays. The sensor network consists of sensor nodes characterized by a directed...
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| Published in | International journal of robust and nonlinear control Vol. 22; no. 3; pp. 331 - 346 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Chichester, UK
John Wiley & Sons, Ltd
01.02.2012
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1049-8923 1099-1239 |
| DOI | 10.1002/rnc.1699 |
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| Abstract | SUMMARY
In this paper, the distributed state estimation problem is investigated for a class of sensor networks described by uncertain discrete‐time dynamical systems with Markovian jumping parameters and distributed time‐delays. The sensor network consists of sensor nodes characterized by a directed graph with a nonnegative adjacency matrix that specifies the interconnection topology (or the distribution in the space) of the network. Both the parameters of the target plant and the sensor measurements are subject to the switches from one mode to another at different times according to a Markov chain. The parameter uncertainties are norm‐bounded that enter into both the plant system as well as the network outputs. Furthermore, the distributed time‐delays are considered, which are also dependent on the Markovian jumping mode. Through the measurements from a small fraction of the sensors, this paper aims to design state estimators that allow the nodes of the sensor network to track the states of the plant in a distributed way. It is verified that such state estimators do exist if a set of matrix inequalities is solvable. A numerical example is provided to demonstrate the effectiveness of the designed distributed state estimators. Copyright © 2011 John Wiley & Sons, Ltd. |
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| AbstractList | SUMMARY
In this paper, the distributed state estimation problem is investigated for a class of sensor networks described by uncertain discrete‐time dynamical systems with Markovian jumping parameters and distributed time‐delays. The sensor network consists of sensor nodes characterized by a directed graph with a nonnegative adjacency matrix that specifies the interconnection topology (or the distribution in the space) of the network. Both the parameters of the target plant and the sensor measurements are subject to the switches from one mode to another at different times according to a Markov chain. The parameter uncertainties are norm‐bounded that enter into both the plant system as well as the network outputs. Furthermore, the distributed time‐delays are considered, which are also dependent on the Markovian jumping mode. Through the measurements from a small fraction of the sensors, this paper aims to design state estimators that allow the nodes of the sensor network to track the states of the plant in a distributed way. It is verified that such state estimators do exist if a set of matrix inequalities is solvable. A numerical example is provided to demonstrate the effectiveness of the designed distributed state estimators. Copyright © 2011 John Wiley & Sons, Ltd. In this paper, the distributed state estimation problem is investigated for a class of sensor networks described by uncertain discrete‐time dynamical systems with Markovian jumping parameters and distributed time‐delays. The sensor network consists of sensor nodes characterized by a directed graph with a nonnegative adjacency matrix that specifies the interconnection topology (or the distribution in the space) of the network. Both the parameters of the target plant and the sensor measurements are subject to the switches from one mode to another at different times according to a Markov chain. The parameter uncertainties are norm‐bounded that enter into both the plant system as well as the network outputs. Furthermore, the distributed time‐delays are considered, which are also dependent on the Markovian jumping mode. Through the measurements from a small fraction of the sensors, this paper aims to design state estimators that allow the nodes of the sensor network to track the states of the plant in a distributed way. It is verified that such state estimators do exist if a set of matrix inequalities is solvable. A numerical example is provided to demonstrate the effectiveness of the designed distributed state estimators. Copyright © 2011 John Wiley & Sons, Ltd. |
| Author | Liang, Jinling Wang, Zidong Liu, Xiaohui |
| Author_xml | – sequence: 1 givenname: Jinling surname: Liang fullname: Liang, Jinling email: jinlliang@gmail.com organization: Department of Mathematics, Southeast University, Nanjing 210096, People's Republic of China – sequence: 2 givenname: Zidong surname: Wang fullname: Wang, Zidong organization: Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, U.K – sequence: 3 givenname: Xiaohui surname: Liu fullname: Liu, Xiaohui organization: Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB8 3PH, U.K |
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| References_xml | – reference: Zhao Y, Wu J, Shi P. H∞ control of nonlinear dynamic systems: a new fuzzy delay fractioning approach. IET Control Theory and Applications 2009; 3(7):917-928. – reference: Brooks R, Ramanathan P, Sayeed AM. Distributed target classification and tracking in sensor networks. Proceedings of the IEEE 2003; 91(8):1163-1171. – reference: Xia Y, Zhu Z, Mahmoud MS. H2 control for networked control systems with Markovian data losses and delays. ICIC Express Letters 2009; 3(3(A)):271-276. – reference: Speranzon A, Fischione C, Johansson KH, Sangiovanni-Vincentelli A. A distributed minimum variance estimator for sensor networks. IEEE Journal on Selected Areas in Communications 2008; 26(4):609-621. – reference: Zhang H, Feng G, Duan G, Lu X. ℋ︁∞ filtering for multiple-time-delay measurements. IEEE Transactions on Signal Processing 2006; 54(5):1681-1688. – reference: Chen W-M, Li C-S, Chiang F-Y, Chao H-C. Jumping ant routing algorithm for sensor networks. Computer Communications 2007; 30:2892-2903. – reference: Busch C, Magdon-Ismail M, Mavronicolas M. Efficient bufferless packet switching on trees and leveled networks. Journal of Parallel and Distributed Computing 2007; 67(11):1168-1186. – reference: Liu Y, Wang Z, Liang J, Liu X. Stability and synchronization of discrete-time Markovian jumping neural networks with mixed mode-dependent time delays. IEEE Transactions on Neural Networks 2009; 20(7):1102-1116. – reference: Liu Y, Wang Z, Liu X. State estimation for discrete-time Markovian jumping neural networks with mixed mode-dependent delays. Physics Letters A 2008; 372:7147-7155. – reference: Boukas EK, Liu ZK. Robust H∞ control of discrete-time Markovian jump linear systems with mode-dependent time-delays. IEEE Transactions on Automatic Control 2001; 46:1918-1924. – reference: Cattivelli FS, Sayed AH. Diffusion strategies for distributed Kalman filtering and smoothing. IEEE Transactions on Automatic Control 2010; DOI: 10.1109/TAC.2010.2042987. – reference: Vesely V, Quang TN. Robust output networked control system design. ICIC Express Letters 2010; 4(4):1399-1407. – reference: Yu W, Chen G, Wang Z, Yang W. Distributed consensus filtering in sensor networks. IEEE Transactions on Systems, Man, and Cybernetics-B 2009; 39(6):1568-1577. – reference: Liu Y, Wang Z, Liang J, Liu X. Synchronization and state estimation for discrete-time complex networks with distributed delays. IEEE Transactions on Systems, Man, and Cybernetics 2008; 38(5):1314-1325. – reference: Martin D, del Toro R, Haber R, Dorronsoro J. Optimal tuning of a networked linear controller using a multi-objective genetic algorithm and its application to one complex electromechanical process. International Journal of Innovative Computing, Information and Control 2009; 5(10(B)):3405-3414. – reference: Zhu X, Hua C, Wang S. State feedback controller design of networked control systems with time delay in the plant. International Journal of Innovative Computing, Information and Control 2008; 4(2):283-290. – reference: Xiong Z, Liveris AD, Cheng S. Distributed source coding for sensor networks. IEEE Signal Processing Magazine 2004; 80-94. – reference: Cattivelli FS, Sayed AH. Diffusion LMS strategies for distributed estimation. IEEE Transactions on Signal Processing 2010; 58(3):1035-1048. – reference: Wu L, Shi P, Gao H, Wang C. ℋ︁∞ filtering for 2D Markovian jump systems. Automatica 2008; 44(7):1849-1858. – reference: Xiong J, Lam J. Fixed-order robust ℋ︁∞ filter design for Markovian jump systems with uncertain switching probabilities. IEEE Transactions on Signal Processing 2006; 54(4):1421-1430. – reference: Gao H, Chen T. H∞ estimation for uncertain systems with limited communication capacity. IEEE Transactions on Automatic Control 2007; 52(11):2070-2084. – reference: Gunawan I. 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In this paper, the distributed state estimation problem is investigated for a class of sensor networks described by uncertain discrete‐time dynamical... In this paper, the distributed state estimation problem is investigated for a class of sensor networks described by uncertain discrete‐time dynamical systems... |
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| SubjectTerms | distributed state estimation Markovian chain mode-dependent distributed delays parameter uncertainties sensor network |
| Title | Distributed state estimation for uncertain Markov-type sensor networks with mode-dependent distributed delays |
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