## Analysis, Control and Applications of Passivity in Complex Networks

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WanRu Wang, LianKun Sun, HongRu Gu, "*H*_{∞} Filter Design for Networked Control Systems: A Markovian Jump System Approach", *Discrete Dynamics in Nature and Society*, vol. 2020, Article ID 3436461, 8 pages, 2020. https://doi.org/10.1155/2020/3436461

*H*_{∞} Filter Design for Networked Control Systems: A Markovian Jump System Approach

**Academic Editor:**Hao Shen

#### Abstract

This paper puts forward a method to design the *H*_{∞} filter for networked control systems (NCSs) with time delay and data packet loss. Based on the properties of Markovian jump system, the packet loss is treated as a constant probability independent and identically distributed Bernoulli random process. Thus, the stochastic stability condition can be acquired for the filtering error system, which meets an *H*_{∞} performance index level *γ*. It is shown that, by introducing a special structure of the relaxation matrix, a linear representation of the filter meeting an *H*_{∞} performance index level for NCSs with time delay and packet loss can be obtained, which uses linear matrix inequalities (LMIs). Finally, numerical simulation examples demonstrate the effectiveness of the proposed method.

#### 1. Introduction

As a new generation of control systems, NCSs [1–10] have attracted more and more researchers’ attention because of their extensive application. Compared with the traditional control system, NCSs have some advantages, such as easy wiring, installation, maintenance, expansion, reliability, and flexibility, so that resource sharing is achieved in such systems. However, the network also brought some new problems, in which time delay and packet dropout are two main aspects, which will not only make a negative impact on system but also may even lead to the instability of system. Recently, the problem of NCSs with time delay and packet dropout phenomenon has become a hot topic in the control field [11–13].

The Markovian jump system (MJS) refers to a stochastic system with multiple model states and the system transitions between modes in accordance with the properties of the Markov chain due to the multimode transition characteristics of the Markovian jump system in the actual engineering. It can be used to simulate many systems with abrupt characteristics, such as manufacturing systems and fault-tolerant systems [14–24]. In [25], the exponential *L*_{2}–*L*_{∞} filter problem of the linear system is explored, and the system has both distributed delay, Markovian jump parameter, and norm bounded parameter uncertainty. In [26], the *H*_{∞} filtering design about a continuous MJS in distributed sampled-data asynchronous is involved; in addition, the system’s mode jumping instants and filter are asynchronous. In [27], the design of a sampled system *H*_{∞} filter is studied. However, many conclusions only consider time delay or packet loss separately, which is not very consistent with the actual situation of network application. In addition, the *H*_{∞} filter design of NCSs with time delay and packet dropout has not yet been considered widely.

Based on this, this paper studies the stability of networked control systems considering both time delay and packet loss. Although the analysis process is more complicated than considering time delay or packet loss alone, however the conclusion is more general and universal, and then the existence conditions of system filters are given. The effectiveness of the proposed method is verified by simulation, and the relevant conclusions are more practical. In Section 2, NCSs with time delay are present by a Markov model. The two probabilities of packet dropout in the Bernoulli random process is designed. In Section 3, according to Lyapunov’s stability theorem, the *H*_{∞} performance of system is proven. In Section 4, a *H*_{∞} filter for NCSs with time delay and packet loss is designed. In Section 5, numerical simulation examples are given to verify the result of this paper. In Section 6, we make a conclusion.

Notation: here are some of the symbols in the paper. The superscript “*T*” means the matrix transposition, and shows the *n*_{x}-dimensional Euclidean space. *I* denotes the unit matrix of adaptive dimension, and 0 refers to the zero matrix of adaptive dimension. indicates is real symmetric and positive matrix, and the notation stands for the norm of matrix that is defined according to , where “tr” denotes the trace operator. indicates the usual Euclidean vector norm. Prob stands for the occurrence probability of the event “·”. *E*{*x*} and represent the expectation of event *x* and the expectation of *x* conditional on *y*, respectively.

#### 2. Problem Formulation

First, consider the following kind of discrete-time linear systems [28] with time delay aswhere refers to the state vector in the plant; belongs to *l*_{2}[0, ∞), which indicates the measured output; shows the estimating intended signal; and *A*(*r*(*k*)) indicates system parameters depend on *r*(*k*). *B*(*r*(*k*)), *C*(*r*(*k*)), *D*(*r*(*k*)), and *L*(*r*(*k*)) have a similar situation. *R*(*k*) is assumed to a discrete Markov chain, and the values of it are in a finite set with a transition probabilities matrix ; set and , such that system mode variable *r*(*k*) satisfies , where and ; *h* has *N* Markov modes, and for , *A*(*r*(*k*)) are denoted by *A*_{i} with appropriate dimensions. *B*_{i}, *C*_{i}, *D*_{i}, and *L*_{i} also have the same situation. shows a Bernoulli-distributed sequence with relationship as follows:

After calculations, another important expectation can be shown as follows:

*Remark 1. *For network packet loss, both Bernoulli distribution and Poisson distribution have been considered. According to the network protocols adopted in actual systems, such as industrial Ethernet and profibus, it is more practical to model network packet loss with Bernoulli distribution in this paper.

Mathematical model description of filtering is in the following formula:where and are the state vector of filter mode estimator and indicates the output vector of the estimator. *A*_{f i}, *B*_{f i}, *C*_{f i}, and *D*_{f i} are real matrices to be determined with compatible dimensions. Combining (4) and (1), a filter error system with Markov chain can be shown aswhereObviously, a filter error system (5) is a Markovian jump system with packet loss. We will use some important definitions in the following for essential later steps.

*Definition 1. *(see [29]). A filter error system (5) is stochastically stable when *ω*(*k*) = 0. There and , and the following inequality exists:

*Definition 2. *(see [29]). Give a scalar *γ* > 0, assume that system (5) is stochastically stable, and (5) also can meet an *H*_{∞} performance index level γ under zero conditions for all nonzero if it satisfies

#### 3. Main Results and Proofs

Based on the previous known conditions, a stochastically stable condition meets an *H*_{∞} performance index level *γ* for NCSs with the development of time delay and packet loss.

Theorem 1. *If there exists symmetric matrices P_{i} and R_{i} > 0, consider NCSs with filter in (5); when ω(k) = 0, system (5) will be stochastically stable such thatwhere .*

*Proof. *Consider a Lyapunov functional candidate as follows:and nextwhere andApplying Schur complement theorem to the above ,From Theorem 3, we can getwhere *λ*_{min} (-Λ) indicates the minimum value of the eigenvalue for -Λ and *ε* = inf {*λ*_{min} (-Λ)} is the lower bound of *λ*_{min} (-Λ); for any *M* ≥ 1, we can obtainThus, we can getAs a result, it can be proved that system (5) will be stochastically stable. The proof is completed.

Theorem 2. *For NCSs (5), give a scalar γ > 0. For all ω(k) ≠ 0, if there are symmetric matrices P_{i}, R_{i} > 0 satisfied the following matrix inequalities, and the system (5) will be stochastically stable, which meets the H_{∞} norm performance level γ:*

*Proof. *When *ω*(*k*) ≠ 0 is similar to Theorem 3 proof process, we obtain the following equation:where ,in whichBy Schur complement, (19) is equivalent to the following formula:where , and it can obtain thatinitial conditions *V*(0) = 0 and *E*{*V*(∞)} ≥ 0; system (5) meets *H*_{∞} norm performance level *γ*, and it can clearly see thatThe proof is over.

#### 4. Filter Design

Here, we will go to solve the system filter.

Theorem 3. *Consider NCSs (5) with a scalar γ > 0. If symmetric matrix P_{1i} > 0, P_{3i} > 0, Q_{1i} > 0, Q_{3i} > 0, X_{i} > 0, and Y_{i} > 0 and P_{2i}, Q_{2i}, Z_{i}, A_{Fi}, B_{Fi}, C_{Fi}, and D_{Fi} satisfied the following matrix inequalities, thenin which*

System (5) meets *H*_{∞} norm performance level *γ*, which is stochastically stable. Then, the filter which is achieved by the desired *γ* is calculated by .

*Proof. *Slack matrix approach can be used for (17) by settingThen, we have following equation using (26):According to equations in (17) and (27), we can getThe proof is over.

#### 5. Simulation Result

Consider system (5) with

Two models in the simulation are set as those data in (29). In the model, the transition probabilities are the initial value of is 0.5, and the initial condition is set to zero. It is easily observed that the method is feasible, and the system with time delay and packet loss becomes stochastically stable, which meets *H*_{∞} norm performance level *γ*. Some simulation results are shown in Figures 1–5.

Figure 1 shows the time response of *r*(*t*).

Figure 2 is the parameter change of the system error . It can be seen from the figure that, with the passage of time, changes from a large fluctuation to a stable state one.

Figure 3 shows the values of and . It can be seen from the figure that and remain stable after about 20 seconds.

Figure 4 is the parameter change of the filter state and . From the figure, it can be seen that the value of gradually becomes equal to the value of overtime and finally both values remain stable.

Figure 5 is the parameter change of and . From the figure, it can be seen that the initial time of fluctuates greatly, and it tends to be consistent with in the end.

*Remark 2. *As can be seen from the ordinate of the system state diagram of Figure 3 and the filter state diagram of Figure 4, the difference between the two is 100 times, so the change of appears to be small in Figure 5. This also reflects from one side that the filter designed in this paper has a smaller overshoot and more stable output.

#### 6. Conclusion

This paper has investigated the *H*_{∞} filtering problem about NCSs with time delay and packet dropout phenomenon. Packet dropout is treated as a constant probability independent and identically distributed Bernoulli random process. By the Lyapunov stability theory, system (5) meeting *H*_{∞} norm performance level *γ* is proven. By introducing a special structure of the relaxation matrix, the solution of filter which meets an *H*_{∞} performance index level of NCSs with time delay and packet dropout is completed. Finally, a simulation result is given to prove the validity of the new design scheme.

#### Data Availability

The data used to support the findings of this study are included within the article. Because it is a numerical simulation example, readers can get the same results as this article by using the LMI toolbox of Matlab and the theorem given in this article.

#### Conflicts of Interest

The authors declare that they have no conflicts of interest.

#### Acknowledgments

This work was supported by the National Natural Science Foundation of China (Grant nos. 61403278 and 61503280).

#### References

- L. Sun and N. Xu, “Stability analysis of Markovian jump system with multi-time-varying disturbances based on improved interactive convex inequality and positive definite condition,”
*IEEE Access*, vol. 7, pp. 54910–54917, 2019. View at: Publisher Site | Google Scholar - H. Zhang, Q. Hong, H. Yan, F. Yang, and G. Guo, “Event-based distributed
*H*_{∞}filtering networks of 2-DOF quarter-car suspension systems,”*IEEE Transactions on Industrial Informatics*, vol. 13, no. 1, pp. 312–321, 2017. View at: Publisher Site | Google Scholar - X. Li, B. Zhang, P. Li, Q. Zhou, and R. Lu, “Finite-horizon
*H*_{∞}state estimation for periodic neural networks over fading channels,”*IEEE Transactions on Neural Networks and Learning Systems*, pp. 1–11, 2019. View at: Publisher Site | Google Scholar - H. Yan, F. Qian, H. Zhang, F. Yang, and G. Guo, “
*H*_{∞}fault detection for networked mechanical spring-mass systems with incomplete information,”*IEEE Transactions on Industrial Electronics*, vol. 63, no. 9, pp. 5622–5631, 2016. View at: Publisher Site | Google Scholar - X.-M. Li, Q. Zhou, P. Li, H. Li, and R. Lu, “Event-triggered consensus control for multi-agent systems against false data injection attacks,”
*IEEE Transactions on Cybernetics*, pp. 1–11, 2019. View at: Publisher Site | Google Scholar - L. Cao, H. Li, G. Dong, and R. Lu, “Event-triggered control for multi-agent systems with sensor faults and input saturation,”
*IEEE Transactions on Systems, Man and Cybernetics: Systems*, pp. 1–12, 2019. View at: Publisher Site | Google Scholar - H. Y. Li, Y. B. Gao, P. Shi, and H.-K. Lam, “Observer-based fault detection for nonlinear systems with sensor fault and limited communication capacity,”
*IEEE Transactions on Automatic Control*, vol. 61, no. 9, pp. 2745–2751, 2015. View at: Publisher Site | Google Scholar - H. C. Yan, Q. Yang, H. Zhang, F. Yang, and X. Zhan, “Distributed
*H*_{∞}state estimation for a class of filtering networks with time-varying switching topologies and packet losses,”*IEEE Transactions on System, Man, and Cybernetics: Systems*, vol. 48, no. 12, pp. 2047–2057, 2018. View at: Publisher Site | Google Scholar - H.-D. Tran, Z.-H. Guan, X.-K. Dang, X.-M. Cheng, and F.-S. Yuan, “A normalized PID controller in networked control systems with varying time delays,”
*ISA Transactions*, vol. 52, no. 5, pp. 592–599, 2013. View at: Publisher Site | Google Scholar - H. Zhang, X. Y. Zheng, H. C. Yan, C. Peng, Z. Wang, and Q. Chen, “Codesign of event-triggered and distributed
*H*_{∞}filtering for active semi-vehicle suspension systems,”*IEEE Transactions on Mechatronics*, vol. 22, no. 2, pp. 1047–1058, 2017. View at: Publisher Site | Google Scholar - Q. X. Zhu, K. H. Lu, and Y. Zhu, “Observer-based feedback control of networked control systems with delays and packet dropouts,”
*Journal of Dynamic Systems Measurement and Control*, vol. 138, no. 2, pp. 2–11, 2016. View at: Publisher Site | Google Scholar - J. Wang, L. Shen, J. W. Xia, Z. Wang, and X. Chen, “Asynchronous dissipative filtering for nonlinear jumping systems subject to fading channels,”
*Journal of the Franklin Institute*, vol. 357, no. 1, pp. 589–605, 2020. View at: Publisher Site | Google Scholar - Q. X. Zhu, K. H. Lu, and Y. Zhu, “
*H*_{∞}guaranteed cost control for networked control systems under scheduling policy based on predicted error,”*Mathematical Problems in Engineering*, vol. 2014, Article ID 586029, 12 pages, 2014. View at: Publisher Site | Google Scholar - H. Y. Li, P. Shi, D. Y. Yao, and L. Wu, “Observer-based adaptive sliding mode control for nonlinear Markovian jump systems,”
*Automatica*, vol. 64, pp. 133–142, 2016. View at: Publisher Site | Google Scholar - G. Nagamani and S. Ramasamy, “Dissipativity and passivity analysis for uncertain discrete-time stochastic Markovian jump neural networks with additive time-varying delays,”
*Neurocomputing*, vol. 174, pp. 795–805, 2016. View at: Publisher Site | Google Scholar - M. C. Dai, J. W. Xia, H. Xia, and H. Shen, “Event-triggered passive synchronization for Markov jump neural networks subject to randomly occurring gain variations,”
*Neurocomputing*, vol. 331, pp. 403–411, 2019. View at: Publisher Site | Google Scholar - L. Shen, X. F. Yang, J. Wang, and J. Xia, “Passive gain-scheduling filtering for jumping linear parameter varying systems with fading channels based on the hidden Markov model,”
*Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering*, vol. 233, no. 1, pp. 67–79, 2019. View at: Publisher Site | Google Scholar - X. Y. Yin, X. Zhang, L. Zhang, C. Wang, M. Al-Yami, and T. Hayat, “
*H*_{∞}model approximation for discrete-time Takagi-Sugeno fuzzy systems with Markovian jumping parameters,”*Neurocomputing*, vol. 157, pp. 306–314, 2015. View at: Publisher Site | Google Scholar - H. Y. Li, H. J. Gao, P. Shi, and X. Zhao, “Fault-tolerant control of Markovian jump stochastic systems via the augmented sliding mode observer approach,”
*Automatica*, vol. 50, no. 7, pp. 1825–1834, 2014. View at: Publisher Site | Google Scholar - M. C. Dai, Z. G. Huang, J. W. Xia, B. Meng, J. Wang, and H. Shen, “Non-fragile extended dissipativity-based state feedback control for 2-D Markov jump delayed systems,”
*Applied Mathematics and Computation*, vol. 362, Article ID 124571, 2019. View at: Publisher Site | Google Scholar - Y. C. Ma, M. H. Chen, and Q. L. Zhang, “Non-fragile static output feedback control for singular TTS fuzzy delay-dependent systems subject to Markovian jump and actuator saturation,”
*Journal of the Franklin Institute*, vol. 353, no. 11, pp. 2373–2397, 2016. View at: Publisher Site | Google Scholar - W. H. Qi and X. W. Gao, “State feedback controller design for singular positive Markovian jump systems with partly known transition rates,”
*Applied Mathematics Letters*, vol. 46, pp. 111–116, 2015. View at: Publisher Site | Google Scholar - L. Qiu, B. Zhang, G. Xu, J. Pan, and F. Yao, “Mixed
*H*_{2}/*H*_{∞}control of Markovian jump time-delay systems with uncertain transition probabilities,”*Information Sciences*, vol. 373, pp. 539–556, 2016. View at: Publisher Site | Google Scholar - B. P. Jiang, C. C. Gao, Y. G. Kao, and Z. Liu, “Sliding mode control of Markovian jump systems with incomplete information on time-varying delays and transition rates,”
*Applied Mathematics and Computation*, vol. 290, pp. 66–79, 2016. View at: Publisher Site | Google Scholar - B. Y. Zhang and Y. M. Li, “Exponential
*L*_{2}–*L*_{∞}filtering for distributed delay systems with Markovian jumping parameters,”*Signal Processing*, vol. 93, no. 1, pp. 206–216, 2013. View at: Publisher Site | Google Scholar - X. H. Ge and Q. L. Han, “Distributed sampled-data asynchronous
*H*_{∞}filtering of Markovian jump linear systems over sensor networks,”*Signal Processing*, vol. 127, pp. 86–99, 2016. View at: Publisher Site | Google Scholar - G. Mustafa and T. W. Chen, “
*H*_{∞}filtering for nonuniformly sampled systems: a Markovian jump systems approach,”*Systems & Control Letters*, vol. 60, no. 10, pp. 871–876, 2011. View at: Publisher Site | Google Scholar - N. Xu and L. K. Sun, “An improved
*H*_{∞}filtering for Markovian jump system with time-varying delay based on finite frequency domain,”*Journal of the Franklin Institute*, vol. 356, no. 12, pp. 6724–6740, 2019. View at: Publisher Site | Google Scholar - F. W. Li, P. Shi, X. C. Wang, and H. R. Kamiri, “Robust
*H*_{∞}filtering for networked control systems with Markovian jumps and packet dropouts,”*Modelling, Identification and Control*, vol. 35, no. 3, pp. 159–168, 2014. View at: Publisher Site | Google Scholar

#### Copyright

Copyright © 2020 WanRu Wang 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.