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Shenping Xiao, Liyan Wang, Hongbing Zeng, Lingshuang Kong, Bin Qin, "Robust Filtering for Networked Control Systems with Random Sensor Delay", Mathematical Problems in Engineering, vol. 2014, Article ID 710952, 7 pages, 2014. https://doi.org/10.1155/2014/710952
Robust Filtering for Networked Control Systems with Random Sensor Delay
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.
Networked control systems (NCSs) which are new control systems where sensor-controller and controller-actuator signal link is through a real time network . 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 , 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 , where sector nonlinearities and parameter uncertainties are also studied. In , 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 . 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  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 ). For any positive matrix , and for differentiable signal in , the following inequality holds: where
Lemma 2 (see ). For any positive matrix , scalar , and a vector function such that the integration is well defined, then
Lemma 3 (see ). 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
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 withwhere
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 , 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:
Then, the filtering problem is solvable. Moreover, the parameter matrices of the filter are given by
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.
4. Simulation Example
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.
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.
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.
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