Mathematical Problems in Engineering

Volume 2012 (2012), Article ID 945271, 19 pages

doi:10.1155/2012/945271

Research Article

Network-Based Robust Filtering for the Uncertain Systems with Sensor Failures and Noise Disturbance

Mingang Hua,1,2,3 Pei Cheng,⁴ Juntao Fei,¹ Jianyong Zhang,⁵ and Junfeng Chen¹

¹College of Computer and Information, Hohai University, Changzhou 213022, China

²Changzhou Key Laboratory of Sensor Networks and Environmental Sensing, Changzhou 213022, China

³Jiangsu Key Laboratory of Power Transmission and Distribution Equipment Technology, Changzhou 213022, China

⁴School of Mathematical Sciences, Anhui University, Hefei 230601, China

⁵Department of Mathematics and Physics, Hohai University, Changzhou 213022, China

Received 24 September 2012; Revised 20 November 2012; Accepted 24 November 2012

Academic Editor: Zhan Shu

Copyright © 2012 Mingang Hua 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.

Abstract.

The network-based robust filtering for the uncertain system with sensor failures and the noise is considered in this paper. The uncertain system under consideration is also subject to parameter uncertainties and delay varying in an interval. Sufficient conditions are derived for a linear filter such that the filtering error systems are robust globally asymptotically stable while the disturbance rejection attenuation is constrained to a given level by means of the performance index. These conditions are characterized in terms of the feasibility of a set of linear matrix inequalities (LMIs), and then the explicit expression is then given for the desired filter parameters. Two numerical examples are exploited to show the usefulness and effectiveness of the proposed filter design method.

1. Introduction

Networked control system (NCS) is a new control system structure where sensor-controller and controller-actuator signal link is through a shared communication network. Therefore, networked control systems have become an active research area in recent years in [1–3]. Recently, the filter design for networked systems become an active research area due to the advantages of using networked media in many aspects such as low cost, reduced weight and power requirements, simple installation and maintenance, and high reliability in [4–10]. On the other hand, with the increasing of the working time in the domain of industry, some parts of the control system (e.g., actuator and sensor) can always be invalid. By time-scale decomposition, the reliable control for linear time-invariant multiparities singularly perturbed systems against sensor failures is studied in [11]. For systems with both state and input time delays, a novel state and sensor fault observer is proposed to estimate system states and sensor faults simultaneously in [12]. A new robust filtering problem is investigated for a class of time-varying nonlinear system with norm-bounded parameter uncertainties, bounded state delay, sector-bounded nonlinearity, and probabilistic sensor gain faults in [13]. The robust output feedback controller design for uncertain delayed systems with sensor failure and time delay is considered in [14]. The problems of robust fault estimation and fault-tolerant control for Takagi-Sugeno fuzzy systems with time delays and unknown sensor faults are addressed in [15]. The robust filtering problem for a class of discrete time-varying Markovian jump systems with randomly occurring nonlinearities and sensor saturation is studied in [16]. The robust infinite-horizon filtering problem for a class of uncertain nonlinear discrete time-varying stochastic systems with multiple missing measurements and error variance constraints is considered in [17]. The problem of distributed filtering in sensor networks using a stochastic sampled-data approach is investigated in [18]. The problems of stability analysis, performance analysis, and robust filter design for uncertain Markovian jump linear systems with time-varying delays are considered in [19].

In distributed industrial and military NCSs, sensors can be in a hostile environment and subject to failure and malfunction. Recently, the filtering problem for NCSs has received considerable attention. The problem of designing filters for a class of nonlinear networked control systems with transmission delays and packet losses is investigated in [20]. The control problems of networked control system with fault/failure of sensors and actuators are also received attention. The reliable control of a class of nonlinear NCSs via T-S fuzzy model with probabilistic sensor and actuator faults/failures, measurement distortion, time-varying delay, packet loss, and norm-bounded parameter uncertainties is investigated in [20]. Recently, based on T-S fuzzy model, the robust and reliable filter design for a class of nonlinear networked control systems is investigated with probabilistic sensor failure in [21]. The reliable filtering problem for network-based linear continuous-time system with sensor failures has been studied in [22]. However, the proposed filer design approach [21, 22] do not consider the systems with uncertainty. The time delay has restriction when the rate of delay is differential, which is only applicable to unknown rate of time delay. No delay-dependent filtering results on the uncertain networked control systems with sensor failures and disturbance noise are available in the literature, which motivates the present study.

In this paper, based on the delay-dependent stability criteria proposed in [23], a delay-dependent performance analysis result is established for the filtering error systems. A new sensor failure model with uncertainties is proposed, and a new different Lyapunov functional is then employed to deal with systems with sensor failures and uncertainties. As a result, the filter is designed in terms of linear matrix inequalities (LMIs), which involves fewer matrix variables but has less conservatism. The resulting filters can ensure that the filtering error system is asymptotically stable and the estimation error is bounded by a prescribed level for all possible bounded energy disturbances, which has advantages over the results of [22] in that it involves fewer matrix variables but has less conservatism. Meanwhile, the parameter uncertainties for system with sensor failures and the noise are considered in this paper, which are more general cases. Finally, two examples are given to show the effectiveness of the proposed method. This paper is organized as follows. Section 2 describes the system model and presents the definition and some lemmas. The robust filter design method is derived in Section 3. Section 4 includes two simulation examples.

2. Problem Description

Throughout this paper, denotes the -dimensional Euclidean space, and is the set of real matrices. is the identity matrix, stands for the induced matrix 2 norm, and stands for the transpose of the matrix . For symmetric matrices and , the notation (resp., means that the is positive definite (resp., positive semidefinite). denotes a block that is readily inferred by symmetry.

Consider the following uncertain systems:

where is the state vector, is the measurable output vector, the noise disturbance is external plant belongs to , and is a signal to be estimated. , , , and are known real constant matrices, , , and are unknown matrices representing time-varying parameter uncertainties, and the admissible uncertainties are assumed to be modeled in the form:

where , and are known constant matrices, is unknown time-varying matrices with Lebesgue measurable elements bounded by

Assumption 1

The considered NCS consists of a time-driven sensor.

Assumption 2

There exist some sensor failures in the feedback channel.

Considering the effect of the common network on the data transmission, the filter can be expressed as

where represents the state estimate, is the output with sensor failures, is the estimated output, , and are the filter parameters to be designed. denotes the sampling period, and are some integers such that . is the time from the instant when sensors sample from the plant to the instant when actuators send control actions to the plant. Obviously, , .

Remark 2.1. In (2.4), is a subset of . Moreover, it is not required that . When , it means that no packet dropout occurs in the transmission. If , there are dropped packets but the received packets are in ordered sequence. If , it means out-of-order packet arrival sequences occur. If , it implies that , which includes and as special cases, where is a constant.

Remark 2.2. Since , define , which denotes the time-varying delay in the control signal. Obviously,

which implies that

where and denote infimum of and supremum of , respectively.

Our aim in this paper is to design a robust filter in the form of (2.4) such that

(i)system (2.1) is said to be robust globally asymptotically stable, subject to , for all admissible uncertainties satisfying (2.2)-(2.3);(ii)for the given disturbance attenuation level and under zero initial condition, the performance index satisfies the following inequality:

For an easy exposition of our results, we first consider the following systems with no uncertain parameters:

The switch matrix for filter (2.4) is introduced against sensor failures as follows:

where

From above analysis and (2.4), then we can get the output of the sensor failures

so the filter (2.4) under consideration is of the following structure:

Define and the filter errors , then the filtering error system can be represented as follows:

where

Throughout this paper, we use the following lemmas.

Lemma 2.3 (see [24]). Given constant matrices , , and with appropriate dimensions, where and , then

if and only if

Lemma 2.4 (see [25]). For any constant matrix , scalar , vector function such that the integrations are well defined, the following inequality holds:

Lemma 2.5 (see [26]). For given matrices , , and with and scalar , the following inequality holds:

3. Main Results

Section 3.1 provides an performance condition for the filtering error system (2.13). Design of filter for the system (2.8) with no uncertainty will be developed in Section 3.2, and robust filter design for the uncertain system (2.1) will be developed in Section 3.3.

3.1. Performance Analysis of Filter

Theorem 3.1. Consider the system in (2.8). For a specified filter (2.12) and constants and , the filtering error system (2.13) is globally asymptotically stable with performance if there exist real matrices , , , , , and , such that the following LMIs are satisfied:

where

Proof. Consider the Lyapunov-Krasovskii functional candidate as follows:

Calculating the time derivative of along the trajectory of system (3.4), one has

where

By using Lemma 2.3, we have that

On the other hand,

Set

Then

Combining (3.8)–(3.10) and by Lemma 2.4, we have

Combining (3.5), (3.7), and (3.11) yields

where

Under the zero-initial condition, one can obtain that and . Define

then, for any nonzero ,

where

Due to , our elaborate estimation induces a convex domain of matrices , which are negative definite if and only if and . By using Lemma 2.3, the LMIs (3.1) and (3.2) can guarantee . Since , it implies that , and thus, . That is, .

Second, we also can prove that under the condition of Theorem 3.1, the filtering error system (2.13) with is globally asymptotically stable. This completes the proof.

3.2. Design of Filter

Now based on the previous result, we are in a position to present the main result in this paper, which offers a new networked-based filter design approach for the system (2.8).

Theorem 3.2. Consider the system in (2.8). A filter of form (2.12) and constants and , the filtering error system is globally asymptotically stable with performance , if there exist real matrices , , , , , , and any matrices , , and such that the following LMIs are satisfied:

where

Moreover, if the previous conditions are satisfied, an acceptable state-space realization of the filter is given by

Proof. Defining

then and . Choose the LKF candidate as (3.4), and set

Pre- and postmultiplying (3.1) and (3.2) by and , respectively, we can obtain that (3.1) is equivalent to (3.17), and (3.2) is equivalent to (3.18). Thus, we can conclude from Theorem 3.2 that the error systems are globally asymptotically stable with the attenuation level . In addition, the filter matrices , , and can be constructed from (3.21). This completes the proof.

Remark 3.3. When , and are given, matrix inequalities (3.17) and (3.18) are linear matrix inequalities in matrix variables , , , , , , , , , , and , which can be efficiently solved by the developed interior point algorithm [24]. Meanwhile, it is esay to find the minimal attenuation level .

Remark 3.4. From the proof process of Theorem 3.1, one can clearly see that neither model transformation nor bounding technique for cross terms is used. Therefore, the obtained filter design method is expected less conservative. It is well known that the number of variables has a great influence on the computation burden. The number of variables involved in the LMIs (3.17)–(3.19) is . However, the numbers of variables in [22] is . With much fewer matrix variables Theorem 3.2 also saves much computation than Theorem  2 in [22].

3.3. Robust Filter

On the basis of the result of Theorem 3.2, it is easy to obtain the network-based robust filter design for the uncertain systems (2.1) with uncertainties , , and satisfying (2.2)-(2.3).

Theorem 3.5. Consider the uncertain system in (2.1). A filter of form (2.12) and constants and , the filtering error system is robust globally asymptotically stable with performance , if there exist real matrices , , ,  , , , and and any matrices , , and , and scalars and such that the following LMIs and (3.19) are satisfied:

where

Moreover, if the previous conditions are satisfied, an acceptable state-space realization of the filter , , and are given by (3.21).

Proof. Replace , and in the LMIs (3.17) and (3.18) with , and , respectively, then the LMIs (3.17) and (3.18) can be rewritten as

where

By Lemma 2.5, there exist scalars and such that

then by Lemma 2.3, (3.24) follows directly.

Remark 3.6. When the is unknown, by setting , Theorem 3.2 and Theorem 3.5 reduce to a delay-dependent and rate-independent network-based robust filter design condition for the uncertain systems (2.1) with uncertainties , , and satisfying (2.2)-(2.3).

4. Numerical Example

In this section, two examples are given to illustrate the effectiveness and benefits of the proposed approach.

Example 4.1. Consider the system (2.8) with [22]

This example has been considered in [22], and assume that satisfies , and the sensor has a probabilistic distort, that is, the distort matrix . Note that different and yield different , to compare with the existing results [22]; we assume that is unknown, and by setting in Theorem 3.2, the computation results of under different and are listed in Tables 1 and 2. Minimum index for different and is listed in Table 3. From Tables 1–3, it can be seen that the value of grows for and for given , which tends to be 0.2143.

To get minimum index , the approach in [22] needs 70 decision variables; however, the number of decision variables involved in Theorem 3.2 is only 29, which sufficiently demonstrates the efficiency of the proposed method. With fewer matrix variables the minimum index obtained in this paper are less conservative than those in [22].

Table 1: Minimum index for unknown and = 0.01.

0.2
0.5
1
5
10
20

[22]
0.2143
0.2143
0.2143
0.2143
0.2143
0.2143

Theorem 3.2
0.1361
0.1900
0.2067
0.2140
0.2142
0.2143

Table 2: Minimum index for unknown and = 0.

0.1
0.5
1
5
10
20

[22]
0.2143
0.2143
0.2143
0.2143
0.2143
0.2143

Theorem 3.2
0.0884
0.1903
0.2068
0.2140
0.2142
0.2143

Table 3: Minimum index for different and = 0.

0.1
0.5
1
5
10
20

= 0
0.0797
0.1733
0.1949
0.2122
0.2137
0.2141

= 0.5
0.0884
0.1851
0.2005
0.2125
0.2137
0.2142

≥ 1
0.0884
0.1903
0.2068
0.2140
0.2142
0.2143

unknown
0.0884
0.1903
0.2068
0.2140
0.2142
0.2143

The initial conditions and are and , respectively, for an appropriate initial interval. For given with , according to Theorem 3.2, we can obtain the desired filter parameters as follows:

Next, we apply the filter (2.12) with the filter matrices (3.21) to the system (2.8) and obtain the simulation results as in Figures 1–3. Figure 1 shows the state response under the initial condition. Figure 2 shows error response . Figure 3 shows the the output and . From these simulation results, we can see that the designed filter can stabilize the system (2.8) with sensor failures and noise disturbance.

Figure 1: The state response of system in Example 4.1.

Figure 2: The error response in Example 4.1.

Figure 3: The output and in Example 4.1.

The example conclusively shows that our results are less conservative than the previous ones in [22].

Example 4.2. Consider the uncertain system (2.1) with

and the uncertainties of the system are of the forms (2.2) and (2.3) with

We assume satisfies , , and the sensor has a probabilistic distort, that is, the distort matrix

When is unknown, by setting in Theorem 3.5, the minimum achievable noise attenuation level is given by and the correspond filter parameters as follows:

When , by Theorem 3.5, the minimum achievable noise attenuation level is given by and the correspond filter parameters as follows:

The initial conditions and are and , respectively, for an appropriate initial interval. Next, we apply the filter (2.12) with the filter matrices (3.21) to the uncertain system (2.1) and obtain the simulation results as in Figures 4–6. Figure 4 shows the state response under the initial condition. Figure 5 shows error response . Figure 6 shows the the output and . From these simulation results, we can see that the designed filter can stabilize the system (2.1) with sensor failures and noise disturbance.

Figure 4: The state response of system in Example 4.2.

Figure 5: The error response in Example 4.2.

Figure 6: The output and in Example 4.2.

5. Conclusions

In this paper the network-based robust filtering for the uncertain system with sensor failures and noise disturbance has been developed. A new type of Lyapunov-Krasovskii functional has been constructed to derive a less conservative sufficient condition for a linear full-order filter in terms of LMIs, which guarantees a prescribed performance index for the filtering error system. Two numerical examples have shown the usefulness and effectiveness of the proposed filter design method. Finally, our future study will focus mainly on the following two issues: to further improve our results by using the delay decomposition LKF. When the noise is stochastic, that is to say, the network-based robust filtering for the uncertain system with sensor failures and stochastic noise could be considered.

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