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Complexity, Dynamics, Control, and Applications of Nonlinear Systems with Multistability 2021

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Research Article | Open Access

Volume 2021 |Article ID 2260753 | https://doi.org/10.1155/2021/2260753

Guobao Liu, Shibin Shen, Xianglei Jia, "Dissipative Filter Design for Nonlinear Time-Varying-Delay Singular Systems against Deception Attacks", Complexity, vol. 2021, Article ID 2260753, 15 pages, 2021. https://doi.org/10.1155/2021/2260753

Dissipative Filter Design for Nonlinear Time-Varying-Delay Singular Systems against Deception Attacks

Academic Editor: Sundarapandian Vaidyanathan
Received08 Sep 2021
Revised25 Oct 2021
Accepted30 Oct 2021
Published24 Nov 2021

Abstract

This paper applies a T-S fuzzy model to depict a class of nonlinear time-varying-delay singular systems and investigates the dissipative filtering problem for these systems under deception attacks. The measurement output is assumed to encounter random deception attacks during signal transmission, and a Bernoulli distribution is used to describe this random phenomena. In this case, the filtering error system modeled by a stochastic singular T-S fuzzy system is established and stochastic admissibility for this kind of system is defined firstly. Then, by combining some integral inequalities and using the Lyapunov–Krasovskii functional approach, sufficient delay-dependent conditions are presented based on linear matrix inequality techniques, where the system of filtering error can be stochastically admissible and strictly -dissipative against randomly occurring deception attacks. Moreover, parameters of the desired filter can be obtained via the solutions of the established conditions. The validity of our work is illustrated through a mostly used example of the nonlinear system.

1. Introduction

In order to study various actual systems, such as large-scale systems, circuit systems, and biological systems, scholars have widely used the singular system model to describe these systems. In contrast with ordinary state space systems, singular systems can describe the performance characteristics of physical systems better [1]. On the other hand, time delay is a major factor that leads to system instability and performance degradation. Because of the coupling of the delay term and the functional equation, the study of singular time-delay systems is much more difficult than that of the standard time-delay systems [2]. Not only stability but also regularity and absence of impulse (or causality) are involved in the admissible analysis problem for singular time-delay systems. Up to now, various results for studying singular time-delay systems have been published. Reachable set estimation for continuous-time singular delay systems [35], sliding mode control for discrete-time singular delay systems [68], and control for singular time-delay systems with Markovian jump parameters [911] are just a few examples of so many research works.

In the past decade, control researchers discovered that in practical or industrial applications, nonlinearities in system dynamics behavior can be fairly accurately described as a set of locally linear models mixed together with fuzzy membership functions. Thus, any complex nonlinear systems can be fuzzified and effectively approximated as a set of linear models by using the T-S fuzzy model approach [12]. In this case, a similar way to linear systems can be extended to analyze and synthesize nonlinear systems and lots of fruitful studies on the fields of stability theories, adaptive tracking control, filter design, etc., have been achieved for T-S fuzzy systems [1318]. Recently, using the T-S fuzzy model to approximate singular nonlinear systems has also been published in many literature studies. To mention a few, the problems of admissibility analysis and controller design for singular T-S fuzzy systems with mismatched membership functions were investigated in [19,20]; the problems of adaptive sliding mode controller design for T-S fuzzy singular systems were considered in [21,22]; and asynchronous filtering problems for T-S fuzzy singular systems with Markovian jump parameters were investigated in [23,24].

On another active research frontier, cyber-attacks have become more and more important factors to threaten network security in the network control system since they could lead to the leakage of a large amount of confidential information. So far, denial-of-service attacks and deception attacks are two main kinds of cyber-attacks widely studied by scholars [2529]. Especially, deception attacks are more secluded and harder to detect which import mendacious data in the process of signal transmission and lead to performance damage of the target system. Therefore, it is very important to design a security filter for the system with deception attacks. For example, the topics include distributed recursive filtering for discrete time-delayed stochastic systems subject to both uniform quantization and deception attacks, recursive filtering for stochastic nonlinear time-varying complex networks with deception attacks, and event-triggered filter design for T-S fuzzy systems with deception attacks had been investigated in [3032], respectively. As far as we know, control and filtering problems for T-S fuzzy singular systems with time-varying delays against deception attacks have not been fully investigated, not mention to dissipative filter design. Dissipativity, in simple terms, generally indicates that the increase in a system’s internal energy storage does not exceed the external energy supply of the system. The dissipativity has been analysed for large amounts of nonlinear systems and widely used in control theory and practice [3335]. All the aforementioned facts motivate our research.

The main contribution of this paper is centered on dealing with the problem of dissipative filtering for T-S fuzzy singular systems with time-varying delays subject to deception attacks, where the measurement output is assumed to encounter random deception attacks based on a Bernoulli distribution during signal transmission. In this case, the filtering error system modeled by a stochastic singular T-S fuzzy system is established and the stochastic admissibility for this kind of system is defined. By using the Lyapunov–Krasovskii functional (LKF) approach and based on linear matrix inequality (LMI) techniques, sufficient delay-dependent conditions are established to guarantee the stochastic admissibility and strictly -dissipativity of the filtering error with randomly occurring deception attacks. Furthermore, based on these feasible conditions, parameters of the desired filter can be obtained. At last, we give an example of the nonlinear system to show the effectiveness of our result.

Notations. In this work, the dimensions of all the matrices are generally considered to be compatible. denotes the Euclidean space with dimension; represents the real matrices with dimension; represents an identity matrix, and 0 denotes a zero matrix with appropriate dimension; denotes the Euclidean norm of a vector and its induced norm of a matrix; is described by ; is the space of integral vector over ; for any real function and real matrix , we define ; the mathematical expectation operator is denoted as .

2. Problem Formulation

For the description of an interval time-varying-delay nonlinear singular system, a delayed T-S fuzzy model is adopted in terms of plant rules as follows.

Rule 1. If is , and , and is , thenwhere , , and stand for the state vector, the measurement output, and the disturbance input, respectively; is the signal to be estimated; is the initial condition; the premise variable vector is described by , and the fuzzy sets are , ; the time-varying delay satisfies the conditions of , , and , , and are constant scalars; may be a singular matrix with ; and , , , , , , and are known real constant matrices with appropriate dimensions.
Then, we can generate the model of the T-S fuzzy singular systems when time-varying delay is considered:whereand stand for the grade of membership for in . It is easy to verify thatIn this paper, for calculating the signal , a fuzzy filter is derived aswhere and represent the filter state and the filter output, respectively. is the sensor measurement under randomly occurring deception attacks which can be given as defines the deception signal imported into the output which is assumed to satisfy , and is considered to be an appropriate dimension matrix; is a Bernoulli-distributed variable and we have the following assumptions on :

Remark 1. The model of deception attacks considered in this paper is established in (6). A Bernoulli distribution is applied to describe the random property of deception attacks. It is easy to say that or means sensor measurement is under deception attacks or not, respectively. Furthermore, the deception attacks are supposed to be norm bounded in (6), since there usually exists an upper bound for the attack signals to avoid detection.

We define the initial condition of system (5) as , and for any , assume that . , , and are filter gains to be determined.

Then, define and . By combining systems (2) and (5), we can obtain the following filtering error system:where

Denotingsystem (8) can be given as

Remark 2. Due to a stochastic variable of Bernoulli distribution, filtering error system (11) is established as a stochastic T-S fuzzy singular system. In this condition, the definition of admissibility in [19] does not work for the stochastic T-S fuzzy singular system in (11) anymore. Motivated by the stochastic admissibility defined in [36] of the discrete time case, we can generalize the definition of admissibility in [19] naturally and have the following definition of stochastic admissibility for system (11).

Definition 1 (see [36]). (1)System (11) is said to be stochastically regular if is not identically zero(2)System (11) is said to be stochastically impulse-free if (3)System (11) with is said to be stochastically stable if there exists a scalar such thatwhere represents the system solution(4)System (11) with is said to be stochastically admissible if it is stochastically regular, impulse-free, and stable

Definition 2 (see [33]). Give real symmetric matrices and and matrix . For a scalar , if the equationholds under zero initial state with , system (11) is said to be strictly --dissipative. Furthermore, we suppose and for some .

Before discussing the main results, we present a few lemmas that we need to use.

Lemma 1 (see [37]). For a matrix , the following inequality holds:where and .

Lemma 2 (see [38]). Given a real scalar , matrices and , and any matrices and , the inequalityholds, where and .

Lemma 3 (see [13]). Ifthen , where ,, satisfy (3) and (4).

3. Main Results

In this section, we will design a dissipative filter for system (1) with randomly occurring deception attacks. First, we give some notations in order to simplify the presentation:

Theorem 1. For given scalars , , , and , matrices and , symmetric matrices and with , and full column rank matrix with , system (11) is said to be stochastically admissible and strictly --dissipative, if there exist matrices , , , , , , , , , , , , and such that the following matrix inequalities hold:where

Proof. First, we will show the stochastic admissibility for system (11) with . It can be obtained from (18) and (19) thatwhere, , and represent matrices which have compatible dimensions and will not be used in the following discussion. Since , , , , , and , we havePremultiplying and postmultiplying (23) by and , we can obtainAccording to Theorem 10.1 of [1] and from (24), we have that the pair is regular and impulse-free. It should be noted thatBy Definition 1, we can conclude system (11) is stochastically regular and impulse-free. Choose the LKF as follows:SetDefine the infinitesimal as . Along the trajectory of (11) with , we haveUsing Lemma 1, it can be generated thatThen, by Lemma 2, we can obtainNote that and . Thus, it is easy to see that for matrices and ,Given matrices and from (11), we haveIt is easy to obtain from (33) thatNoticing , we can obtain that for matrix with appropriate dimension,Considering the definition of deception attacks, it is easy to obtainwhich meansFrom (28)–(37), it can be verified thatwhereWe can obtain from (18) and (19) that . Hence, we can find a scalar so that . Using Dynkin’s formula, it can be derived thatwhich impliesFrom Definition 1, it can be concluded that system (11) in the condition of is considered to be stochastically stable.
To verify the dissipativity for system (11), by (18), (19), and (25), it can be derived thatThus, for any , it is easy to see thatConsidering and under zero initial condition, by (43), we can obtain that (13) holds, which shows that system (11) is strictly dissipative.

Theorem 2. For given scalars , , , and , matrices and , symmetric matrices and with and , and full column rank matrix with , if there are matrices ,