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           Control for Network-Based 2D Systems with Missing Measurements

Xuhui, Bu; Hongqi, Wang; Zheng, Zheng; Wei, Qian

doi:https://doi.org/10.1155/2014/786128

Abstract and Applied Analysis

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Networked Systems with Incomplete Information

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Research Article | Open Access

Volume 2014 | Article ID 786128 | https://doi.org/10.1155/2014/786128

Control for Network-Based 2D Systems with Missing Measurements

Bu Xuhui,¹Wang Hongqi,¹Zheng Zheng,¹and Qian Wei¹

Academic Editor: Jun Hu

Received03 Apr 2014

Revised19 Apr 2014

Accepted20 Apr 2014

Published11 May 2014

Abstract

The problem of control for network-based 2D systems with missing measurements is considered. A stochastic variable satisfying the Bernoulli random binary distribution is utilized to characterize the missing measurements. Our attention is focused on the design of a state feedback controller such that the closed-loop 2D stochastic system is mean-square asymptotic stability and has an disturbance attenuation performance. A sufficient condition is established by means of linear matrix inequalities (LMIs) technique, and formulas can be given for the control law design. The result is also extended to more general cases where the system matrices contain uncertain parameters. Numerical examples are also given to illustrate the effectiveness of proposed approach.

1. Introduction

Two-dimensional (2D) systems have attracted considerable research interest over the past few decades due to their wide applications in the areas such as multidimensional digital filtering, linear image processing, signal processing, process control, and iterative learning control [1–5]. Thus the stability and stabilization of 2D systems have attracted a lot of interests; see, for example, [6–13] and the references therein. optimization is a powerful tool that can be used to design a robust controller or filter, which has been proved to be one of the most important strategies. Recently, such problems on 2D systems have stirred a great deal of research attention. For example, several versions of 2D bounded real lemma have been established in [2, 6, 14]. The problem of control for 2D systems with state delays has been considered in [15]. The problem of filtering for 2D systems has been studied extensively in [16]. An controller is designed for a class of 2D nonlinear discrete systems with sector nonlinearity in [17, 18].

Notice that all the above-mentioned results are based on an implicit assumption that the communication between the physical plant and controller is perfect; that is, the signals transmitted from the plant will arrive at the controller simultaneously and perfectly. However, in many practical situations, there may be a nonzero probability that all the signals can be measured during their transmission. In other words, the systems may have missing measurements. Moreover, networked systems are becoming more and more popular for the reason that they have several advantages over traditional systems, such as low cost, reduced weight and power requirements, simple installation and maintenance, and high reliability [19–22]. If network is introduced to controller design, the data packet dropout phenomenon, which appears in a typical network environment, will naturally induce missing measurements from the plant to the controller. In 1D system, the problems of stability, stabilization, filtering, or state estimation for networked control systems have been widely researched [23–30]. However, in the network-based 2D system case, only few results have been available. For instance, the problem of robust filtering for 2D systems described by the Fornasini-Marchesini (FM) second model with missing measurements is considered in [31]. To the best of the authors’ knowledge, the control for 2D systems represented by the Roesser model which is structurally quite distinct from FM second model has not been addressed in the literature so far.

Motivated by the aforementioned observations, this paper considers the problem of control for network-based 2D systems presented by the Roesser model with missing measurements. We also notice that some dynamical processes such as gas absorption, water stream heating, and air drying can be described by a 2D Roesser model. In practical, these systems are often implemented by distribute control systems (DCSs) or field-bus control systems (FCSs), where control loops that are closed over a communication network. Hence, the considered topic in this paper is of practical significance. Compared with existing results, this paper proposes a state feedback controller design method for 2D systems in the framework of networked control systems. Meanwhile, the introduction of the random missing measurements renders the 2D system to be stochastic instead of a deterministic one. To analyze the stability, we introduce the stochastic mean-square asymptotically stable and stochastic disturbance attenuation level. The controller is also designed under the framework of 2D stochastic systems.

The remainder of this paper is organized as follows. In Section 2, the mathematical description and design objectives of this paper are presented. In Section 3, a sufficient condition of mean-square asymptotic stability with performance for such 2D stochastic systems is derived by means of LMI technique, and then formulas can be given for the control law design. In Section 4, the design result is extended to the 2D systems with uncertain parameters. Numerical examples are given in Section 5 and conclusions are drawn in Section 6.

Notation 1. The superscript “” denotes the matrix transposition, denotes the-dimensional Euclidean space, denotes the identity matrix, 0 denotes the zero vector or matrix with the required dimensions, and denotes the standard (block) diagonal matrix whose off-diagonal elements are zero. In symmetric block matrices, an asteriskis used to denote the term that is induced by symmetry. The notation refers to the Euclidean vector norm and , denote the minimum and the maximum eigenvalue of the corresponding matrix, respectively., mean the expectation of and the expectation of conditional on. Matrices, if the dimensions are not explicitly stated, are assumed to be compatible for algebraic operations.

2. Problem Formulation

Consider the following 2D discrete system given by where, , and represent the horizontal state, vertical state, and control input, respectively.denotes the noise input, which belongs to., , are real matrices with appropriate dimension.

We make the following assumption on the boundary condition.

Assumption 1. The boundary condition is assumed to satisfy

Now, we consider the following state feedback control law: whereis the measurement of state signals,is appropriately dimensioned controller matrix to be determined. When the feedback control is implemented via a networked control system, the dataare transferred as two separate packets from the remote plant to the controller. In this process, the data may be missed due to the network transmission failure, resulting in what we call missing measurement. It is assumed that the data packet dropout can be described by a stochastic variable; that is, where, the stochastic parametersis a Bernoulli distributed white sequence taking the values of 0 and 1 with andis a known constant.

Notice that the introduction of the stochastic variablerenders the 2D system to be stochastic instead of a deterministic one. Before proceeding further, we need to introduce the following definition of stochastic stability for the 2D system, which will be essential for our derivation.

Definition 2 (see [31]). The 2D system (1) is said to be mean-square asymptotically stable if under the zero input and for every bounded initial condition, , the following is satisfied

Definition 3. Given a scalar, the 2D system (1) is said to have an disturbance attenuation lev , if it is mean-square asymptotically stable and under zero initial conditions,is satisfied for any external disturbance , where

To this end, the design objective of this paper can be described as follows.

Problem Statement. For any initial condition satisfying Assumption (1) and missing measurements described as (5), design a state feedback law (3) such that the closed-loop 2D system is mean-square asymptotically stable and has an disturbance attenuation level.

3. Main Results

In this section, we assume that the system matricesand controller gain matrixare known, and then we study the condition under which the closed-loop 2D system is mean-square asymptotically stable with a guaranteed performance. Then, a feasible controller gain matrix can be given based on the condition.

From system (1), (3), and (4), we can obtain the following closed-loop system: Define; it is obvious that Then, the 2D closed-loop system can be rewritten as

Theorem 4. The 2D closed-loop system (10) is mean-square asymptotically stable with a givendisturbance attenuation level, if there exists a positive define symmetric matrix, satisfying where

Proof. We first prove the mean-square asymptotically stability of 2D system (10) with zero disturbanceIn this case, the system becomes Define where.
Consider the following index
Substituting (13) into the above index, we can obtain where From (11), it is easy to see that. Hence, for all, we have where.
Notice that; we have. From (18), it is also easy to see that Hence,and it is independent of . Thus, we obtain, and taking expectation of both sides yields That is, Adding both sides of the inequality system (21) yields Using this relationship iteratively, we can obtain which implies where Now, denote; then, upon the inequality (24) we have Adding both sides of the inequalities yields Then, under Assumption 1, the right side of this inequality is bounded, which means that ; that is, . Then the 2D stochastic system (10) is mean-square asymptotically stable.
Now, theperformance for the 2D stochastic system (10) will be established. To this end, assume zero initial boundary conditions; that is, for all. In this case, the index becomes Another index is introduced as where.
From condition (11), we havefor any; that is,
Taking the expectation of both sides yields
Due to the relationship (31), it can be established that Adding both sides of the inequality system, we have Summing up both sides of this inequality fromto, we have that is, Considering the zero initial boundary conditions, (35) means
This completes the proof.

Remark 5. Theorem 4 provides a sufficient condition of the mean-square asymptotic stability anddisturbance attenuation levelfor 2D systems with missing measurements. If the communication links existing between the plant and the controller are perfect, that is, there is no packet dropout during their transmission, thenand. In this case, the condition in Theorem 4 becomes the condition obtained in [6] for 2D deterministic system. From this point of view, Theorem 4 can be seen as an extension of [6] to 2D systems with missing measurement.
Theorem 4 gives a mean-square asymptotic stability condition with disturbance attenuation levelfor system (10) where the controller gain matrixis known. However, our eventual purpose is to determine a suitableby system matricesand parameter.

The following well-known lemma is needed in the proof of our main result.

Lemma 6 (Schur complement). Assume are given matrices with appropriate dimensions, whereandare positive definite symmetric matrices. Then if and only if or

Based on the above lemma, we can give our main result.

Theorem 7. For the 2D closed-loop system (10), if there exist a positive define symmetric matrixand a matrixsatisfying then the 2D closed-loop system (10) is mean-square asymptotically stability and has andisturbance attenuation level. In this case, a suitable state feedback control law can be given as.

Proof. The condition in Theorem 4 can be rewritten as where
By applying Lemma 6, condition (41) is equivalent to the following LMI condition:
Define , and pro- and postmultiplying for the above condition give Setto obtain the LMI of (40) and the proof is complete.

Remark 8. Theorem 7 provides an LMI condition for the mean-square asymptotic stability anddisturbance attenuation levelof 2D stochastic system, which can be solved by LMI Toolbox. Then by (40), we also can give a suitable state feedback control law. It is noted that LMI (40) only contains few elements; hence, the computation is not big. However, when the system matrices get bigger, the solution of LMI (40) may be time-consuming due to complicated computation. In practical system, the system dimension is not very big (often less than 5). Hence, the computation complexity is acceptable.

Remark 9. For a fixed, the feasibility of (40) is a suboptimalcontroller. Whenis not fixed, the minimization of that satisfies (40) can be searched. Hence, an optimalcontroller can be obtained by solving the following optimization problem:

4. Robust Control for Uncertain 2D Systems

In this section, we extend the above design to the case of robust control. Consider the following 2D system with uncertain parameter perturbations wheredenote admissible uncertain perturbations of matrices, , and, which can be represented as whereare known real constant matrices characterizing the structures of uncertain perturbations andis an uncertain perturbation of the system that satisfies.

Lemma 10. Assumeare matrices or vectors with appropriate dimensions. For any scalarand all matrices with appropriate dimensions satisfying, the following inequality holds: The main result of this section is given in the following theorem.

Theorem 11. For the uncertain 2D system (46) used the state feedback control law (3) with missing measurement, if there exist a positive define symmetric matrix, a matrix, and scalarssatisfying where then the uncertain 2D system is mean-square asymptotically stability and has an disturbance attenuation level. In this case, a suitable state feedback control law can be given as.

Proof. By applying Theorem 7, the uncertain 2D system with state feedback control law (3) is mean-square asymptotically stable and has andisturbance attenuation level, and there exist a positive define symmetric matrix , a matrix satisfying That is, Therefore, using Lemma 10, there exists a scalarsuch that Note that the above condition is equivalent to By the Schur complement, LMI (54) implies LMI in Theorem 11 holds.
This completes the proof.

In addition, by solving the following optimization problem we can obtain a robust optimal controller for the uncertain 2D stochastic systems (46).

Remark 12. This paper considers the problem of stabilization for a class of 2D systems with missing measurements. Here, we describe the missing measurements as a Bernoulli random binary distribution, which renders the 2D systems to be stochastic ones. Under this framework, we give the definition of stochastic mean-square stability and performance, and then a state feedback control design approach is addressed. The proposed design approach is systematic for 2D stochastic system. The results in this paper can be extended to solve other problems such as network-induced delay and filter design.

5. Illustrative Examples

In this section, two numerical examples are used to illustrate the effectiveness of the proposed results.

Example 1. Let us consider 2D system (1) with the following parameters:
It is assumed that measurements transmitted between the plant and the controller are imperfect; that is, the state signal may be lost during their transmission. Suppose; that is, in the communication link, the probability of the data packet missing is 20%. By applying Theorem 7 and solving the optimization problem (45), the minimumdisturbance attenuation level is. Meanwhile, we can obtain Hence, a feasible state feedback control law can be selected as Assume the disturbance is
Simulation results are shown in Figures 1 and 2, where the state response ofis plotted in Figure 1 andis plotted in Figure 2. It can be seen from Figures 1 and 2 that the closed-loop 2D system is asymptotically stable. Hence, even though the 2D system is affected by external disturbances and has significant data dropout in the output measurements, the proposed design approach can guarantee the stability and disturbance attenuation ability for the 2D system.

Example 2. Let us consider uncertain 2D system (46) with the following parameters:
It is also assumed that the probability of the data packet missing is 0.2; that is,. In this case, by applying Theorem 11 and solving the optimization problem (55), the minimumdisturbance attenuation level is. Meanwhile, we can obtain Hence, a feasible state feedback control law can be selected as
The disturbanceis also given as Example 1. Simulation results are shown in Figures 3 and 4, where the state response ofis plotted in Figure 3 andis plotted in Figure 4. It is observed that the closed-loop uncertain 2D system is also asymptotically stable. Hence, the proposed design approach is also effective for uncertain 2D system.

6. Conclusions

In this paper, we have investigated the problem of stabilization for a class of 2D systems described with missing measurements. A sufficient condition has been developed in terms of LMIs, which guarantees mean-square asymptotic stability and disturbance attenuation level for closed-loop 2D system. The result is also extended to more general cases where the system matrices contain uncertain parameters. Numerical examples have been provided to illustrate the effectiveness of proposed approach. In our future work, the control and filtering problems for networked-based 2D system with packet dropouts and network-induced delay will be discussed.

Conflict of Interests

The authors declare that there is no conflict of interests regarding the publication of this paper.

Acknowledgments

This work is supported by the Program of NSFC (no. 61203065, no. 61340015, and no. 61104119), the program of Natural Science of Henan Provincial Education Department (12A510013), the program of Open Laboratory Foundation of Control Engineering Key Discipline of Henan Provincial High Education (KG 2011-10), the program of Key Young Teacher of Henan Polytechnic University, and the Doctoral Fund Program of Henan Polytechnic University (B2012-003).

References

T. Kaczorek, Two-Dimensional Linear Systems, vol. 68 of Lecture Notes in Control and Information Sciences, Springer, Berlin, Germany, 1985.
View at: MathSciNet
C. Du and L. Xie, H_∞ Control and Filtering of Two-Dimensional Systems, vol. 278 of Lecture Notes in Control and Information Sciences, Springer, Berlin, Germany, 2002.
View at: MathSciNet
D. Bors and S. Walczak, “Application of 2D systems to investigation of a process of gas filtration,” Multidimensional Systems and Signal Processing, vol. 23, no. 1-2, pp. 119–130, 2012.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
D. A. Dewasurendra and P. H. Bauer, “A novel approach to grid sensor networks,” in Proceedings of the 15th IEEE International Conference on Electronics, Circuits and Systems (ICECS '08), pp. 1191–1194, September 2008.
View at: Publisher Site | Google Scholar
X. H. Bu, F. S. Yu, Z. Y. Fu, and F. Z. Wang, “Stability analysis of high-order iterative learning control for a class of nonlinear switched systems,” Abstract and Applied Analysis, vol. 2013, Article ID 684642, 13 pages, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
C. Du, L. Xie, and C. Zhang, “ $H_{\infty}$ control and robust stabilization of two-dimensional systems in Roesser models,” Automatica, vol. 37, no. 2, pp. 205–211, 2001.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
X. Guan, C. Long, and G. Duan, “Robust optimal guaranteed cost control for 2D discrete systems,” IEE Proceedings: Control Theory and Applications, vol. 148, no. 5, pp. 355–361, 2001.
View at: Publisher Site | Google Scholar
A. Dhawan and H. Kar, “An LMI approach to robust optimal guaranteed cost control of 2-D discrete systems described by the Roesser model,” Signal Processing, vol. 90, no. 9, pp. 2648–2654, 2010.
View at: Publisher Site | Google Scholar
L. Wu and H. Gao, “Sliding mode control of two-dimensional systems in Roesser model,” IET Control Theory & Applications, vol. 2, no. 4, pp. 352–364, 2008.
View at: Publisher Site | Google Scholar | MathSciNet
Z. Y. Feng, L. Xu, M. Wu, and Y. He, “Delay-dependent robust stability and stabilisation of uncertain two-dimensional discrete systems with time-varying delays,” IET Control Theory & Applications, vol. 4, no. 10, pp. 1959–1971, 2010.
View at: Publisher Site | Google Scholar | MathSciNet
V. Singh, “Stability analysis of 2-D discrete systems described by the Fornasini-Marchesini second model with state saturation,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 55, no. 8, pp. 793–796, 2008.
View at: Publisher Site | Google Scholar
J. L. Liang, Z. D. Wang, Y. R. Liu, and X. H. Liu, “State estimation for two-dimensional complex networks with randomly occurring nonlinearities and randomly varying sensor delays,” International Journal of Robust and Nonlinear Control, vol. 24, no. 1, pp. 18–38, 2014.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
J. L. Liang, Z. D. Wang, X. H. Liu, and L. Panos, “Robust synchronization for 2-D discrete-time coupled dynamical networks,” IEEE Transactions on Neural Networks and Learning Systems, vol. 23, no. 6, pp. 942–953, 2012.
View at: Google Scholar
L. Xie, C. Du, Y. C. Soh, and C. Zhang, “ $H_{\infty}$ and robust control of 2-D systems in FM second model,” Multidimensional Systems and Signal Processing, vol. 13, no. 3, pp. 265–287, 2002.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
J. M. Xu and L. Yu, “ $H_{\infty}$ control for 2-D discrete state delayed systems in the second FM model,” Acta Automatica Sinica, vol. 34, no. 7, pp. 809–813, 2008.
View at: Publisher Site | Google Scholar | MathSciNet
L. Wu, Z. Wang, H. Gao, and C. Wang, “Filtering for uncertain 2-D discrete systems with state delays,” Signal Processing, vol. 87, no. 9, pp. 2213–2230, 2007.
View at: Publisher Site | Google Scholar
S. Ye and W. Wang, “Stability analysis and stabilisation for a class of 2-D nonlinear discrete systems,” International Journal of Systems Science: Principles and Applications of Systems and Integration, vol. 42, no. 5, pp. 839–851, 2011.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
J. T. Dai, Z. Guo, and S. L. Wang, “Robust $H_{\infty}$ control for a class of 2-D nonlinear discrete stochastic systems,” Circuits, Systems, and Signal Processing, vol. 32, no. 5, pp. 2297–2316, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
J. P. Hespanha, P. Naghshtabrizi, and Y. Xu, “A survey of recent results in networked control systems,” Proceedings of the IEEE, vol. 95, no. 1, pp. 138–162, 2007.
View at: Publisher Site | Google Scholar
T. C. Yang, “Networked control system: a brief survey,” IEE Proceedings: Control Theory and Applications, vol. 153, no. 4, pp. 403–412, 2006.
View at: Publisher Site | Google Scholar
J. Wang and K. Zhang, “Nonfragile $H_{\infty}$ control for stochastic systems with Markovian jumping parameters and random packet losses,” Abstract and Applied Analysis, vol. 2014, Article ID 934134, 8 pages, 2014.
View at: Publisher Site | Google Scholar | MathSciNet
W. Zhang, M. S. Branicky, and S. M. Phillips, “Stability of networked control systems,” IEEE Control Systems Magazine, vol. 21, no. 1, pp. 84–99, 2001.
View at: Publisher Site | Google Scholar
Y. L. Wang, Z. R. Xiang, and H. R. Karimi, “Observer-based $H_{\infty}$ control design for nonlinear networked control systems with limited information,” Abstract and Applied Analysis, vol. 2013, Article ID 604249, 9 pages, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
Z. D. Wang, H. L. Dong, B. Shen, and H. Gao, “Finite-horizon $H_{\infty}$ filtering with missing measurements and quantization effects,” IEEE Transactions on Automatic Control, vol. 58, no. 7, pp. 1707–1718, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
Z. D. Wang, B. Shen, H. S. Shu, and G. L. Wei, “Quantized $H_{\infty}$ control for nonlinear stochastic time-delay systems with missing measurements,” IEEE Transactions on Automatic Control, vol. 57, no. 6, pp. 1431–1444, 2012.
View at: Publisher Site | Google Scholar | MathSciNet
J. Hu, Z. Wang, B. Shen, and H. Gao, “Quantised recursive filtering for a class of nonlinear systems with multiplicative noises and missing measurements,” International Journal of Control, vol. 86, no. 4, pp. 650–663, 2013.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet
J. Hu, D. Y. Chen, and J. H. Du, “State estimation for a class of discrete nonlinear systems with randomly occurring uncertainties and distributed sensor delays,” International Journal of General Systems, vol. 43, no. 3-4, pp. 387–401, 2014.
View at: Publisher Site | Google Scholar | MathSciNet
J. Hu, Z. D. Wang, H. Dong, and H. Gao, “Recent advances on recursive filtering and sliding mode design for networked nonlinear stochastic systems: a survey,” Mathematical Problems in Engineering, vol. 2013, Article ID 646059, 12 pages, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
J. Hu, Z. D. Wang, B. Shen, and H. Gao, “Gain-constrained recursive filtering with stochastic nonlinearities and probabilistic sensor delays,” IEEE Transactions on Signal Processing, vol. 61, no. 5, pp. 1230–1238, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
J. Hu, Z. D. Wang, and H. Gao, “Recursive filtering with random parameter matrices, multiple fading measurements and correlated noises,” Automatica, vol. 49, no. 11, pp. 3440–3448, 2013.
View at: Publisher Site | Google Scholar | MathSciNet
X. Liu, H. Gao, and P. Shi, “Robust $H_{\infty}$ filtering for 2-D systems with intermittent measurements,” Circuits, Systems, and Signal Processing, vol. 28, no. 2, pp. 283–303, 2009.
View at: Publisher Site | Google Scholar | Zentralblatt MATH | MathSciNet

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Copyright © 2014 Bu Xuhui 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.

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