- About this Journal ·
- Abstracting and Indexing ·
- Aims and Scope ·
- Annual Issues ·
- Article Processing Charges ·
- Articles in Press ·
- Author Guidelines ·
- Bibliographic Information ·
- Citations to this Journal ·
- Contact Information ·
- Editorial Board ·
- Editorial Workflow ·
- Free eTOC Alerts ·
- Publication Ethics ·
- Reviewers Acknowledgment ·
- Submit a Manuscript ·
- Subscription Information ·
- Table of Contents
Abstract and Applied Analysis
Volume 2014 (2014), Article ID 605214, 6 pages
Fault Detection for Wireless Networked Control Systems with Stochastic Uncertainties and Multiple Time Delays
1School of Automation, Nanjing University of Posts and Telecommunication, Nanjing, Jiangsu 210023, China
2The Chinese People’s Liberation Army of 63961 Troops, Haidian District, Beijing 100012, China
3School of Automation, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
Received 27 January 2014; Accepted 7 April 2014; Published 29 April 2014
Academic Editor: Hao Shen
Copyright © 2014 Lina Rong 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.
The fault detection problem for a class of wireless networked control systems is investigated. A Bernoulli distributed parameter is introduced in modeling the system dynamics; moreover, multiple time delays arising in the communication are taken into account. The detection observer for tracking the system states is designed, which generates both the state errors and the output errors. By adopting the linear matrix inequality method, a sufficient condition for the stability of wireless networked control systems with stochastic uncertainties and multiple time delays is proposed, and the gain of the fault detection observer is obtained. Finally, an illustrated example is provided to show that the observer designed in this paper tracks the system states well when there is no fault in the systems; however, when fault happens, the observer residual signal rises rapidly and the fault can be quickly detected, which demonstrate the effectiveness of the theoretical results.
The network technology has received compelling attention during the past decades . The networked control system, which plays an important role in modern industry such as the car industry and the health care, can usually be classified into the wired networked control system (WNCS) and the wireless networked control system (WiNCS) [2, 3]. Compared with WNCS, WiNCS is a comparatively new technology, which is widely used in military, monitory, and other complex situations. In WiNCS, large numbers of sensor nodes are arranged in the region of interest; due to the characteristics of the wireless communication, information flows among nodes are dynamic. As the structure of WiNCS becomes increasingly modular, system faults may result in fatal damage to the whole system. As a result, the fault detection problem for WiNCS deserves to be investigated.
The fault detection problem for WiNCS has been studied extensively in recent years [4–10]. Reference  considers the fault detection problem for WiNCS with access constraints and random packet dropouts. The residual generation is carried out based on a deterministic formulation and a residual evaluation is proposed by considering the random packet dropouts. In addition, with the help of Chebyshev’s inequality, the fault detection threshold value is obtained. Reference  investigates the fault detection problem for a class of linear time invariant systems with limited network quality of services (QoS). The probabilistic switching between different situations is required to obey a homogeneous Markovian chain. In , the adaptive observer-based fault estimation problem is studied; by exploring the augmented matrix, error dynamic systems are transferred to Markov jumping systems. In , the T-S fuzzy model is adopted to describe the system model, with the benefit that the exact value of network-induced delay is not necessarily known; a fuzzy observer-based approach for the fault detection is developed.
In WiNCS, the sensor nodes gather information from the plant and pass the information to the designed controller via the bus structure. However, as the sensor nodes may be influenced by several unexpected factors such as temperature and moisture, not all the sensor nodes are in the working status. When some nodes are not working, the information flow may be transmitted from other signal channels, which arouses in the uncertainties of WiNCS. On the other hand, as the information transmission is time consuming, time delays arise naturally in WiNCS. The fault detection problem for wireless networked control systems with both stochastic uncertainties and multiple time delays has not been considered in the literature, which motivates the work in this paper. In this paper, we investigate the fault detection for a class of wireless networked control systems. A Bernoulli distributed parameter is introduced in modeling the system dynamics and the detection observer for tracking the system states is designed. By adopting the linear matrix inequality method, a sufficient condition for the stability of wireless networked control systems with stochastic uncertainties and multiple time delays is proposed, and the gain of the fault detection observer is obtained. Finally, an example is given to show the effectiveness of proposed method.
The rest of this paper is organized as follows. In Section 2, we provide the problem formulation. In Section 3, the sufficient condition for the stability of WiNCS is obtained by the linear matrix inequality method. An illustrative example is given in Section 4 and Section 5 is a brief conclusion of this paper.
2. Problem Formulation
Consider a class of WiNCS. The system model is given as follows: where denotes the state without delays, denotes the delayed state of the system, denotes the system input, denotes the fault of the system, denotes the system output, is the stochastic variable, and , , , , , and are constant matrices with appropriate dimensions. The control law has the following form: where is the parameter matrix.
In this paper, the stochastic variable is assumed to be a Bernoulli distributed sequence, which represents whether the communication environment changes or not at each nonnegative integer time . We assume that when there is no change in communication environment, and when the network environment changes. Moreover, the following equations hold: where is a given constant.
In order to generate a residual signal, we design the fault detection observer for model (1) as follows: where and are the state and the output of the observer and is the parameter of the observer to be designed.
The error model of the system is given as
Next, we define the system residual as . Note that if there is no fault in the system, the residual is close to zero. We set up the residual evaluation function and the fault threshold as
By comparing and , it can be decided whether the fault happens or not:
Lemma 1 (Schur complement). For matrices , , and , equals
3. Main Results
In this section, a fault detection observer will be designed based on the Lyapunov stability theory. A sufficient condition for the stability of the system is obtained by the linear matrix inequalities.
Theorem 2. The error system (6) is quadratically stable if there exist matrices , , and with appropriate dimensions satisfying the following inequality: where
Proof. We choose the following Lyapunov function:
Calculate the difference of (12) along system (6); we have
According to Jensen’s inequality, we have
Substituting and into (12), we have where and , , , and are the same as in (10).
According to the Lyapunov stability theory, the error system is stable if
According to Lemma 1, (18) is equivalent to where
We set and multiply on both sides of (19). Then we can get inequality (10). The proof is completed.
Remark 3. As is well known, the randomly occurring phenomena have been extensively investigated in recent years. In this paper, we consider the case where the communication environment is affected by some factors in a probabilistic way described by Bernoulli random variable .
Remark 4. In practice, many systems have stochastic Markovian jumping dynamics [11–15]. Future research efforts will be devoted to the fault detection for wireless sensor networks with stochastic Markovian jumping dynamics.
4. An Illustrative Example
In this section, we will provide a numerical example to illustrate the effectiveness of the theoretical results.
Example 1. Consider system (1), where
We set , and , , and can be obtained as Moreover, we have
When fault happens at sampling time 30, we can see that residual signal rises quickly, which indicates that fault occurs, as shown in Figure 1. In addition, we can clearly see different fault happens in every state from Figure 2.
In this paper, we discussed the fault detection problem for WiNCS with both stochastic uncertainties and multiple time delays. By adopting the Lyapunov method, a sufficient condition for the stability of the system is provided, and the gain of observer is also acquired. Finally, simulation results show the effectiveness of theoretical results.
Conflict of Interests
The authors declare that there is no conflict of interests regarding the publication of this paper.
- R. Wade, W. M. Mitchell, and F. Petter, “Ten emerging technologies that will change the world,” Technology Review, vol. 106, no. 1, pp. 22–49, 2003.
- P. Ögren, E. Fiorelli, and N. E. Leonard, “Cooperative control of mobile sensor networks: adaptive gradient climbing in a distributed environment,” IEEE Transactions on Automatic Control, vol. 49, no. 8, pp. 1292–1302, 2004.
- B. Lincoln and B. Bernhardsson, “Optimal control over networks with long random delays,” in Proceedings of the International Symposium on Mathematical Theory of Networks and Systems, p. 7, Perpignan, France, 2000.
- K. Zhang, B. Jiang, and P. Shi, “Fault estimation observer design for discrete-time takagi-sugeno fuzzy systems based on piecewise lyapunov functions,” IEEE Transactions on Fuzzy Systems, vol. 20, no. 1, pp. 192–200, 2012.
- B. Liu, Y. Xia, Y. Yang, and M. Fu, “Robust fault detection of linear systems over networks with bounded packet loss,” Journal of the Franklin Institute, vol. 349, no. 7, pp. 2480–2499, 2012.
- X. Wan, H. Fang, and S. Fu, “Observer-based fault detection for networked discrete-time infinite-distributed delay systems with packet dropouts,” Applied Mathematical Modelling, vol. 36, no. 1, pp. 270–278, 2012.
- Y.-Q. Wang, H. Ye, S. Ding, and G.-Z. Wang, “Fault detection of networked control systems subject to access constraints and random packet dropout,” Acta Automatica Sinica, vol. 35, no. 9, pp. 1230–1234, 2009.
- 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.
- S. He and F. Liu, “Adaptive observer-based fault estimation for stochastic Markovian jumping systems,” Abstract and Applied Analysis, vol. 2012, Article ID 176419, 11 pages, 2012.
- Y. Zheng, H. Fang, and H. O. Wang, “Takagi-Sugeno fuzzy-model-based fault detection for networked control systems with Markov delays,” IEEE Transactions on Systems, Man, and Cybernetics B: Cybernetics, vol. 36, no. 4, pp. 924–929, 2006.
- S. He and F. Liu, “Robust stabilization of stochastic Markovian jumping systems via proportional-integral control,” Signal Processing, vol. 91, no. 11, pp. 2478–2486, 2011.
- Z. Wu, P. Shi, H. Su, and J. Chu, “Stochastic synchronization of Markovian jump neural networks with time-varying delay using sampled-data,” IEEE Transactions on Cybernetics, vol. 43, no. 6, pp. 1796–1806, 2013.
- Z.-G. Wu, P. Shi, H. Su, and J. Chu, “Passivity analysis for discrete-time stochastic markovian jump neural networks with mixed time delays,” IEEE Transactions on Neural Networks, vol. 22, no. 10, pp. 1566–1575, 2011.
- H. Shen, J. H. Park, L. Zhang, and Z. Wu, “Robust extended dissipative control for sampled-data Markov jump systems,” International Journal of Control, 2013.
- H. Shen, S. Xu, J. Lu, and J. Zhou, “Passivity-based control for uncertain stochastic jumping systems with mode-dependent round-trip time delays,” Journal of the Franklin Institute, vol. 349, no. 5, pp. 1665–1680, 2012.