## New Trends in Networked Control of Complex Dynamic Systems: Theories and Applications

View this Special IssueResearch Article | Open Access

# Robust Filtering for Networked Control Systems with Random Sensor Delay

**Academic Editor:**Huaicheng Yan

#### Abstract

The robust filtering problem for a class of network-based systems with random sensor delay is investigated. The sensor delay is supposed to be a stochastic variable satisfying Bernoulli binary distribution. Using the Lyapunov function and Wirtinger’s inequality approach, the sufficient conditions are derived to ensure that the filtering error systems are exponentially stable with a prescribed disturbance attenuation level and the filter design method is proposed in terms of linear matrix inequalities. The effectiveness of the proposed method is illustrated by a numerical example.

#### 1. Introduction

Networked control systems (NCSs) which are new control systems where sensor-controller and controller-actuator signal link is through a real time network [1]. Because of the advantages, such as convenient fault diagnosis, low cost, and simplicity, the NCSs have been widely applied in many application areas such as industrial automation, remote process, and manufacturing plants. However, the insertion of the communication network may cause time delay, so the signal transferred in NCSs loses the stationary, integrity, and determinacy, which makes the analysis of NCSs become complicate. Therefore, increasing attention has been paid to the study of networked control systems (see, e.g., [2–6] and references therein).

On the other hand, the filtering problem for NCSs has attracted constant research [7–11] since it is important in control engineering and signal processing. In [7], the filtering for NCSs with multiple packet dropouts is considered. The problem of designing filter design for a class of discrete nonlinear NCSs with stochastic time-varying delays and missing measurements is addressed in [9], where sector nonlinearities and parameter uncertainties are also studied. In [10], by using a stochastic sampled-data approach, the problem of distributed filtering in sensor networks is considered. And distributed average filtering for sensor networks with sensor saturation is designed by averagely fusing the information of each local node in [11]. However, there are few literatures to analyze the problem of filtering for continuous-time NCSs with random sensor delay, which motivates the present study.

In this paper, a delay-dependent performance analysis result is derived for the filtering error system and a new random sensor delay model with stochastic parameter matrix is proposed. Combining the reciprocally convex combination technique in [12] and employing Wirtinger’s inequality approach, new criteria are derived for performance analysis, which reduces the conservatism. Based on the derived criteria for performance analysis, the novel filter criteria are obtained in terms of LMIs. Finally, a numerical example is presented to show the effectiveness of the proposed approach.

#### 2. Problem Description

Consider the following networked control systems: where and are the state and measurable output vector, respectively. is the signal to be estimated and is the external disturbance signal belonging to . , and are known matrices with appropriate dimensions.

Consider the following filter for the estimation of : where and are the filter’s state and input vector, respectively. is the estimated output. , and are the filter matrices to be designed.

In the actual networked control systems, the measured output may or may not experience sensor delay, which can be described by two random events:

Assume that the occurrences probability of the above given event can be described as the following formula: Define a stochastic variable : By using Bernoulli distributed sequence, the variable can be assumed to follow an exponential distribution of switching, which satisfies where is a known constant on . Considering the random sensor delay, we suppose that the corresponding measurement is defined as where stands for time-varying delay which satisfies .

Define and ; then the filtering error system can be described as follows: where Our aim in this paper is to design a robust filter in the form of (8) such that(1)system (1) is robustly exponentially stable, subject to ,(2)under zero initial condition and for the disturbance attenuation level , the controlled output satisfies for .

Throughout this paper, we use the following lemmas.

Lemma 1 (see [13]). *For any positive matrix , and for differentiable signal in , the following inequality holds:
**
where
*

Lemma 2 (see [14]). *For any positive matrix , scalar , and a vector function such that the integration is well defined, then
*

Lemma 3 (see [12]). *Let have positive values for arbitrary value of independent variable in an open subset of . The reciprocally convex combination of in satisfies
*

#### 3. Main Results

In this section, a performance condition for the filtering error system (8) and the robust filter design for the system (1) are presented, respectively.

##### 3.1. Performance Analysis of Filter

Theorem 4. *Defining , and , for given positive scalars , the filtering error system (8) is robustly exponentially stable with a norm bound if there exist positive matrices , , , , and proper dimensions matric such that
**
with**where
*

*Proof. *Consider the Lyapunov-Krasovskii functional candidate as

Calculating the time derivative of along the trajectory of (8) yields

By utilizing Lemma 1, the integral term can be estimated as
where

On the other hand, defining and , by the reciprocally convex combination in Lemma 3, the following inequality holds:

Note that due to , according to Lemma 2 and inequalities (22), we have
where

Substituting (20)–(23) into (19) and then applying the Schur complement, it can be concluded that
where

If (25) holds, we have
Carrying out integral manipulations on (27) from 0 to and noting that under zero initial conditions, we obtain
That is, , so the filtering error system has an disturbance attenuation level under zero initial conditions.

Second, we also can prove that the filtering error system with is robustly exponentially stable under the condition of Theorem 4. This completes the proof.

*Remark 5. *Similar to [15], we divide the delay interval into two subintervals uniformly. However, the new Lyapunov-Krasovskii functional in our paper which not only divides the delay interval into two subintervals but also makes use of the information of is proposed. The results will be less conservative.

##### 3.2. Design of Filter

Theorem 6. *Defining , and , for some given constants , and , the filtering error system (8) is robustly exponentially stable with a norm bound if there exist positive matrices , , and and matrices , and of appropriate dimensions such that the following LMIs are satisfied:
**with
**
where
*

Then, the filtering problem is solvable. Moreover, the parameter matrices of the filter are given by

*Proof. *Defining

Applying the Schur complement, it can be concluded that is equivalent to

Set , and .

Pre- and postmultiplying (15) by and give

Thus we can conclude that the filtering error system is robustly exponentially stable with a norm bound . The transfer function of the filter is defined as

According to (32), we can get

Therefore, the parameter matrices of the filter can be chosen as in (32). This completes the proof.

*Remark 7. *According to LMIs (29), we can find that the variable numbers are fewer than Theorem 2 in [16]; therefore, the filter design method provides a more simple form.

#### 4. Simulation Example

*Example 1. *Consider the system described by (1) with the following parameters in [16]:

Assume that satisfies , , and the measured output experiences sensor delay, that is, the sensor delay occurrences probability, . The initial conditions and are and , respectively. According to Theorem 6 with the help from Matlab LMI toolbox, it can be solved that the desired filter parameters are as follows with the performance level :

The simulation results are shown in Figures 1 and 2. Figure 1 shows the error response . The output and are depicted in Figure 2. All the simulations have confirmed that the designed filter can stabilize the system (1) with random sensor delay.

#### 5. Conclusion

In this paper, we have studied the network-based robust filtering problem for continuous-time systems with random sensor delay. A novel Lyapunov-Krasovskii functional has been constructed to design a filter by means of LMIs, which guarantees a prescribed disturbance rejection attenuation level for the filter error system. A numerical example has been provided to show the effectiveness of the proposed filter design method and the input or state delays in the systems should be further considered in the future work.

#### 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 NationalNature Science Foundation of China (61203136, 61304064, and 61074067); this work was supported by Natural Science Foundation of Hunan Province of China (11JJ2038); this work was supported by the Construct Program for the Key Discipline of electrical engineering in Hunan province.

#### References

- G. C. Walsh, H. Ye, and L. G. Bushnell, “Stability analysis of networked control systems,”
*IEEE Transactions on Control Systems Technology*, vol. 10, no. 3, pp. 438–446, 2002. View at: Publisher Site | Google Scholar - H. C. Yan, H. B. Shi, H. Zhang, and F. W. Yang, “Quantized ${H}_{\infty}$ control for networked systems with communication constraints,”
*Asian Journal of Control*, vol. 15, no. 5, pp. 1468–1476, 2013. View at: Google Scholar | MathSciNet - C. Lin, Z. D. Wang, and F. W. 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 Site | Google Scholar | Zentralblatt MATH | MathSciNet - H. Zhang, H. C. Yan, F. W. Yang, and Q. J. Chen, “Quantized control design for impulsive fuzzy networked systems,”
*IEEE Transactions on Fuzzy Systems*, vol. 19, no. 6, pp. 1153–1162, 2011. View at: Publisher Site | Google Scholar - Y. He, G. P. Liu, D. Rees, and M. Wu, “Improved stabilisation method for networked control systems,”
*IET Control Theory and Applications*, vol. 1, no. 6, pp. 1580–1585, 2007. View at: Publisher Site | Google Scholar - H. C. Yan, Z. Z. Su, H. Zhang, and F. W. Yang, “Observer-based ${H}_{\infty}$ control for discrete-time stochastic systems with quantisation and random communication delays,”
*IET Control Theory & Applications*, vol. 7, no. 3, pp. 372–379, 2013. View at: Publisher Site | Google Scholar | MathSciNet - T. C. M. Sahebsara, T. Chen, and S. L. Shah, “Optimal ${H}_{\infty}$ filtering in networked control systems with multiple packet dropouts,”
*Systems & Control Letters*, vol. 57, no. 9, pp. 696–702, 2008. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - X.-M. Zhang and Q.-L. Han, “Network-based ${H}_{\infty}$ filtering for discrete-time systems,”
*IEEE Transactions on Signal Processing*, vol. 60, no. 2, pp. 956–961, 2012. View at: Publisher Site | Google Scholar | MathSciNet - H. L. Dong, Z. D. Wang, and H. J. Gao, “Robust ${H}_{\infty}$ filtering for a class of nonlinear networked systems with multiple stochastic communication delays and packet dropouts,”
*IEEE Transactions on Signal Processing*, vol. 58, no. 4, pp. 1957–1966, 2010. View at: Publisher Site | Google Scholar | MathSciNet - B. Shen, Z. D. Wang, and X. H. Liu, “A stochastic sampled-data 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 - H. Zhang, H. C. Yan, F. W. Yang, and Q. J. Chen, “Distributed average filtering for sensor networks with sensor saturation,”
*IET Control Theory & Applications*, vol. 7, no. 6, pp. 887–893, 2013. View at: Publisher Site | Google Scholar | MathSciNet - P. Park, J. W. Ko, and C. Jeong, “Reciprocally convex approach to stability of systems with time-varying delays,”
*Automatica*, vol. 47, no. 1, pp. 235–238, 2011. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - A. Seuret and F. Gouaisbaut, “On the use of Wirtinger’s inequalities for time-delay systems,” in
*Proceedings of the the 10th IFAC Workshop on Time Delay Systems (IFAC TDS '12)*, Boston, Mass, USA, 2012. View at: Google Scholar - K. Gu, “An integral inequality in the stability problem of time-delay systems,” in
*Proceedings of the 39th IEEE Confernce on Decision and Control*, pp. 2805–2810, December 2000. View at: Google Scholar - X.-M. Zhang and Q.-L. Han, “A less conservative method for designing ${H}_{\infty}$ filters for linear time-delay systems,”
*International Journal of Robust and Nonlinear Control*, vol. 19, no. 12, pp. 1376–1396, 2009. View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet - L. L. Du, Z. Gu, and J. L. Liu, “Network-based reliable H∞ filter designing for the systems with sensor failures,” in
*Proceedings of the International Conference on Modelling, Identification and Control (ICMIC '10)*, pp. 797–800, Okayama, Japan, July 2010. View at: Google Scholar

#### Copyright

Copyright © 2014 Shenping Xiao 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.