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Research article Special Issues

Regularization scheme for uncertain fuzzy differential equations: Analysis of solutions

  • Received: 02 February 2023 Revised: 21 April 2023 Accepted: 26 April 2023 Published: 11 May 2023
  • In this paper a regularization scheme for a family of uncertain fuzzy systems of differential equations with respect to the uncertain parameters is introduced. Important fundamental properties of the solutions are discussed on the basis of the established technique and new results are proposed. More precisely, existence and uniqueness criteria of solutions of the regularized equations are established. In addition, an estimation is proposed for the distance between two solutions. Our results contribute to the progress in the area of the theory of fuzzy systems of differential equations and extend the existing results to the uncertain case. The proposed results also open the horizon for generalizations including equations with delays and some modifications of the Lyapunov theory. In addition, the opportunities for applications of such results to the design of efficient fuzzy controllers are numerous.

    Citation: Anatoliy Martynyuk, Gani Stamov, Ivanka Stamova, Yulya Martynyuk–Chernienko. Regularization scheme for uncertain fuzzy differential equations: Analysis of solutions[J]. Electronic Research Archive, 2023, 31(7): 3832-3847. doi: 10.3934/era.2023195

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  • In this paper a regularization scheme for a family of uncertain fuzzy systems of differential equations with respect to the uncertain parameters is introduced. Important fundamental properties of the solutions are discussed on the basis of the established technique and new results are proposed. More precisely, existence and uniqueness criteria of solutions of the regularized equations are established. In addition, an estimation is proposed for the distance between two solutions. Our results contribute to the progress in the area of the theory of fuzzy systems of differential equations and extend the existing results to the uncertain case. The proposed results also open the horizon for generalizations including equations with delays and some modifications of the Lyapunov theory. In addition, the opportunities for applications of such results to the design of efficient fuzzy controllers are numerous.



    Networked control systems (NCSs) have been widely studied and applied in many fields in the past few decades, including DC motor [1], intelligent transportation [2], teleoperation [3], etc. Compared with traditional feedback control systems, the components in NCSs transmit data packets through communication networks, which reduce wiring requirements, save installation costs, and improve the maintainability [4]. However, public and open communication networks are vulnerable to malicious attacks by hackers. Cyber attacks can not only disrupt data transmission but also indirectly cause controlled plants to malfunction and stop operating by injecting fake data [5]. For example, in 2011, the "Stuxnet" virus invaded Iran's Bolshevik nuclear power plant, causing massive damage to its nuclear program [6]; in 2015, the "BlackEnergy" trojan virus successfully attacked Ukraine's power companies and cut off the local power supply [7]. Therefore, the security of NCS under cyber attacks has received increasing attention, and many illuminating results have been reported (see surveys [8,9]).

    Current cyber-attacks may be roughly divided into two categories: denial of service (DoS) attacks and deception attacks. DoS attacks can cause network congestion or paralysis by sending a large amount of useless request information, thereby blocking the transmission of signals [10,11]. Compared to the simple and direct blocking of signal transmission in DoS attacks, deception attacks are more subtle and difficult to detect. More specifically, deception attacks compromise data integrity by hijacking sensors to tamper with measurement data and control signals. In some cases, they may be more damaging than DoS attacks [12]. Therefore, many researchers have conducted analysis and synthesis of NCSs under deception attacks. For instance, Du et al. [13] presented stability conditions for wireless NCSs subjected to deception attacks on the data link layer, and determined the maximum permissible deception attack time. Hu et al. [14] examined discrete-time stochastic NCSs with deception attacks and packet loss, and gave security analysis and controller design. In [15], Gao et al. investigated nonlinear NCSs faced with several types of deception attacks, and proposed asynchronous observer design strategies. Notably, the existing literature characterized the random occurrence of deception attacks by introducing a stochastic variable that obeys the Bernoulli distribution with a fixed probability, which is over-limited in reality.

    In previous literature on NCSs, control tasks are often executed in a fixed/variable period, giving rise to the so-called sampled-data control (time-triggered control) [16,17,18,19]. Although this control scheme is simple and easy to implement, it has two shortcomings. On the one hand, to stabilize the system in some extreme scenarios, it is necessary to set a small sampling interval, which results in a large number of redundant data packets and may cause network congestion. On the other hand, due to the lack of a judgment condition for the system state, even if the stabilization of the system is achieved, the sampling task will not stop, which is undoubtedly a waste of network resources. In contrast to the time-triggered scheme (TTS), the event-triggered scheme (ETS) can significantly conquer these shortcomings and save communication resources [20,21,22]. In the ETS, the execution frequency of the control task is limited by introducing an event generator. That is, only when the change in system state exceeds a given threshold, an event will be generated and the controller will execute [23,24,25]. Based on the above facts, a multitude of studies have been conducted on event-triggered control for NCSs suffering from deception attacks (see [26,27,28,29,30]). However, most ETS designs under deception attacks adopted the periodic event-triggered scheme (PETS). The PETS can avoid the Zeno phenomenon by periodically checking the event trigger conditions but inevitably lose some system state information. To resolve this contradiction, Selivanov et al. proposed a switched event-triggered scheme (SETS) [31]. By switching between TTS and continuous event triggering [32], the SETS not only avoids the Zeno phenomenon, but also fully utilizes the system information, thereby further reducing the number of event triggers [33,34,35,36].

    Inspired by the above facts, the subject of this study is SETS-based stabilization for NCSs under random deception attacks. By means of the Lyapunov function method, a sufficient condition is developed to assure that the close-loop system is mean square exponentially (MSE) stable. Then, a co-design of the trigger and feedback-gain matrices for the SETS-based controller is presented in terms of linear matrix inequalities (LMIs), which can be checked easily. The major contributions can be outlined as follows: 1) A new SETS (NSETS), which additionally introduces a term concerning the last event triggering time, is designed. In contrast to the SETS in [31], the NSETS can further reduce the number of trigger times while maintaining performance. 2) Compared with the existing literature (see, e.g., [28,29,30]), the occurrence of deception attacks under our consideration is assumed to be a time-dependent stochastic variable that obeys the Bernoulli distribution with probability uncertainty, which is more realistic. 3) Stability analysis criterion and computationally tractable controller design strategy are presented by utilizing a piecewise-defined Lyapunov function together with a few matrix inequalities.

    Notation. In this paper, we use Rm×n and Rm to denote m×n-dimensional real matrices and m-dimensional Euclidean space, respectively. For each ZRm×n, we use Z<0 (Z0) to indicate that the matrix Z is real symmetric negative definite (semi-negative definite), S{Z} to denote the sum of Z+ZT, and λM(Z) and λm(Z) to represent the maximum eigenvalue and the minimum eigenvalue of the symmetric matrix Z, respectively. We use ||||, Pr, and E to represent the Euclidean norm, probability operator, and expectation operator, respectively. In addition, we use diag{} to denote a diagonal matrix and to denote a symmetric term in a symmetric matrix.

    In this section, we first give the system description, deception-attack model, and NSETS design, and then formulate the switched closed-loop system.

    Consider a linear time-invariant system (LTIS) described as

    ˙x(t)=Ax(t)+Bu(t). (2.1)

    In system (2.1), A and B are known system matrices of suitable dimensions, x(t)Rnx, u(t)Rnu, denote the state, control input, respectively.

    In the event-triggered NCS, an event generator is used to determine whether the lately sampled data should be forwarded to the controller. When cyber-attacks do not happen, any state signals that satisfy the triggering condition can be received successfully by the controller, and the corresponding control signals can be received successfully by the actuator. However, when the communication network is subject to deception attacks, the control signal will be substituted with fake data, the performance of the controller will inevitably decline, and even lead to system instability in some extreme cases.

    In view of the openness of network protocols, it is quite possible that attackers can inject the information transmitted in NCSs with fake data. In this paper, we suppose that the deception signal d(x(t)) satisfying Assumption 1 will totally substitute the original transmission data.

    Assumption 1. [26] The signal of deception attack d(x(t)) satisfies

    ||d(x(t))||||Dx(t)||, (2.2)

    where D is a real constant matrix.

    To demonstrate the randomness of deception attacks, a Bernoulli variable β(t){0,1} with uncertain probability is introduced:

    Pr(β(t)=1)=α+Δα(t), Pr(β(t)=0)=1αΔα(t). (2.3)

    Clearly, E{β(t)}=α+Δα(t). Then, the signal transmitted under deception attacks can be presented as

    ˜x(t)=(1β(t))d(x(t))+β(t)x(t).

    Remark 2.1. Motivated by [26,27,28,29,30], the deception attacks are assumed to be randomly occurring. However, different from these references, this paper supposes that the probability of deception attack occurrence allows for some uncertainty; that is, there is a small positive constant ˉα such that ||Δα(t)||ˉα.

    Taking into consideration the constrained network resources, a NSETS is devised as follows:

    tσ+1=min{ttσ+h[x(t)x(tσ)]TM[x(t)x(tσ)]>ε1xT(t)Mx(t)+ε2xT(tσ)Mx(tσ)}, (2.4)

    where tσ is the last triggering moment with σN, M>0 is a trigger matrix to be determined, h>0 and εi (i{1,2}) are predefined parameters that stand for the waiting interval and triggering thresholds, respectively.

    Remark 2.2. Inspired by [31,33,34,35,36], the SETS is used in this paper. However, unlike the existing SETSs, the proposed NSETS introduces an additional term ε2xT(tσ)Mx(tσ) into the trigger condition to more efficiently utilize the state information at the current moment and previous triggering moment. The introduced ε2xT(tσ)Mx(tσ) increases the difficulty of the event triggering and, therefore, prolongs the interval between triggered events.

    In accordance with the proposed NSETS, the control input of system (2.1) under deception attacks with uncertain probability can be designed as

    u(t)=K(1β(t))d(x(t))+Kβ(t)x(tσ), (2.5)

    where K is the feedback gain that remains to be determined.

    Then, we introduce T1σ=[tσ,tσ+h) and T2σ=[tσ+h,tσ+1), under event-triggered controller (2.5), system (2.1) can be characterized as

    {˙x(t)=[A+BKβ(t)]x(t)+BK(1β(t))d(x(t))BKβ(t)ttσ˙x(s)ds,tT1σ,˙x(t)=[A+BKβ(t)]x(t)+BK(1β(t))d(x(t))+BKβ(t)e(t),tT2σ, (2.6)

    where σN and e(t)=x(tσ)x(t) satisfying

    eT(t)Me(t)ε1xT(t)Mx(t)+ε2xT(tσ)Mx(tσ). (2.7)

    Remark 2.3. Unlike general ETSs, the controlled system with the SETS needs to switch between periodic sampling and continuous event triggering to ensure the performance of the system. Inspired by [31], we denote the sampling error and triggering error by ttσ˙x(s)ds and e(t), respectively, and rewrite the controlled system as switched system (2.6) with two different modes.

    Next, we introduce a definition of mean square exponential stability and several lemmas.

    Definition 2.1 ([37]). System (2.6) is said to be MSE stable if there exist two scalars c1>0 and c2>0 such that

    E{||x(t)||}c1ec2tE{||x0||},t0,

    Lemma 2.2 ([38]). For any positive definite matrix S Rn×n, scalars a, b (a>b), and a vector function η:[a,b]Rn, we have

    [abη(δ)dδ]TS[abη(δ)dδ](ab)abηT(δ)Sη(δ)dδ.

    Lemma 2.3 ([39]). For given a, b Rn, and positive definite matrix QRn×n, we have

    2aTbaTQa+bTQ1b.

    Lemma 2.4 ([40]). For a prescribed constant ϑ>0 and real matrices Π, Xi, Yi, and Zi(i=1,,n), if

    [ΠXYZ]<0

    holds, where XY=[X1+ϑY1,X2+ϑY2,,Xn+ϑYn] and Z=diag{S{ϑZ1},S{ϑZ2},,S{ϑZn}}, then we have

    Π+ni=1S{XiZ1iYTi}<0. (2.8)

    Now, the issue of event-triggered control in response to deception attacks can be expressed more specifically as follows: given a LTIS in (2.1), determine the NSETS-based controller in (2.5) such that, for all admissible deception attacks d(x(t)), the switched closed-loop system in (2.6) is MSE stable.

    In this section, we first establish the exponential stability criterion for system (2.1), then develop a joint design method for the trigger matrix and feedback gain.

    To be consistent with the switched closed-loop system (2.6), we construct the following Lyapunov function:

    V(t)={V1(t)=Vp(t)+Vq(t)+Vu(t),tT1σ,V2(t)=Vp(t),tT2σ (3.1)

    where

    Vp(t)=xT(t)Px(t),Vq(t)=(tσ+ht)ttσe2θ(ts)˙xT(s)Q˙x(s)ds,Vu(t)=(tσ+ht)[x(t)x(tσ)]T[S{U1}2U1+U2S{U12U2}][x(t)x(tσ)],

    and P, Q are symmetric positive matrices, U1, U2 are general matrices, and θ is a positive constant.

    Based on Lyapunov function (3.1), an exponential stability criterion of system (2.6) can be established, which is provided as follows:

    Theorem 3.1. For given positive scalars ε1, ε2, h, θ, α, ˉα and matrices K, D, under the NSETS (2.4) and random deception attacks, system (2.6) is MSE stable, if there exist symmetric matrices P>0, Q>0, M>0, Λi>0 (i{1,2,3,4}), and general matrices U1, U2, W1, W2, W3, N1,1, N2,1, N1,2, N2,2, such that

    Σ=[P+hS{U12}hU1+hU2hS{U2U12}]>0, (3.2)
    Σ1=[Ω11Ω12Ω3]<0, (3.3)
    Σ2=[Ω21Ω22Ω3]<0, (3.4)
    Σ3=[Ω31Ω32Ω33]<0 (3.5)

    hold true, where

    Ω11=[Ω11,1Ω11,2Ω11,3Ω1,4Ω12,2Ω12,3Ω2,4Ω13,30I]+ˉα2RT1(Λ1+Λ2)R1,R1=[I00I],Ω21=[Ω21,1Ω21,2Ω21,3Ω1,4Ω1,5S{N2,1}WT2Ω2,4Ω2,5Ω23,30hWT3I0Ω5,5]+ˉα2RT2(Λ1+Λ2)R2,R2=[I00IhI],R3=[I0II],Ω31=[Ω31,1Ω31,2Ω31,3Ω31,4S{N2,2}Ω32,3Ω32,4Ω33,30I]+ˉα2RT3(Λ3+Λ4)R3,Ω12=[ΩT1,60000ΩT2,700]T,Ω32=[Ω3T1,50000Ω3T2,600]T,Ω22=[Ω120], Ω3=[Λ100Λ2],Ω33=[Λ300Λ4],Ω11,1=S{NT1,1(A+BKα)W1+(2θh1)U12}+2θP+DTD,Ω21,1=Ω11,1θhS{U1},Ω11,2=PW2NT1,1+h2S{U1}+(A+BKα)TN2,1,Ω21,2=Ω11,2h2S{U1},Ω11,3=WT1W3+(12θh)(U1U2), Ω21,3=Ω11,3+2θh(U1U2),Ω1,4=(1α)NT1,1BK, Ω1,5=h(WT1αNT1,1BK), Ω1,6=NT1,1BK,Ω12,2=hQS{N2,1}, Ω12,3=WT2h(U1U2),Ω2,4=(1α)NT2,1BK, Ω2,5=h(WT2αNT2,1BK),Ω2,7=NT2,1BK, Ω5,5=he2θhQ,Ω13,3=S{W3+(2θh1)(U12U2)},Ω23,3=Ω13,3S{2θh(U12U2)},Ω31,1=2θP+S{NT1,2(A+BKα)}+(ε1+ε2)M+DTD,Ω31,2=PNT1,2+(A+BKα)TN2,2, Ω31,3=NT1,2BKα+ε2M,Ω31,4=NT1,2BK(1α), Ω31,5=NT1,2BK, Ω32,3=αNT2,2BK,Ω32,4=(1α)NT2,2BK, Ω32,6=NT2,2BK, Ω33,3=(ε21)M.

    Proof. Since

    Vp(t)+Vu(t)=xT(t)Px(t)+(tσ+ht)[x(t)x(tσ)]T[S{U1}2U1+U2S{U12U2}][x(t)x(tσ)]=tσ+hth[x(t)x(tσ)]TΣ[x(t)x(tσ)]+ttσh[x(t)x(tσ)]T[P00][x(t)x(tσ)], (3.6)

    which, in conjunction with P>0 and Σ>0, ensures the positive definiteness of Vp(t) + Vu(t). From (3.1) and (3.6), we have

    min{λm(P),λm(Σ)}||x(t)||2V(t). (3.7)

    For any tT1σ, σN, differentiating V1(t) along the trajectories of the system (2.6) and taking mathematical expectations on it yields:

    E{˙V1(t)}=E{˙Vp(t)}+E{˙Vq(t)}+E{˙Vu(t)},

    where

    E{˙Vp(t)}=E{2θVp(t)}+2θxT(t)Px(t)+2xT(t)P˙x(t),E{˙Vq(t)}=E{2θVq(t)}ttσe2θ(ts)˙xT(s)Q˙x(s)ds+(tσ+ht)˙xT(t)Q˙x(t),E{˙Vu(t)}=E{2θVu(t)}+(tσ+ht)[˙xT(t)S{U1}x(t)+2˙xT(t)(U1+U2)x(tσ)]+[2θ(tσ+ht)1]×[xT(t)S{U1}2x(t)+2xT(t)(U1+U2)x(tσ)+xT(tσ)S{U12U2}x(tσ)].

    Thus

    E{˙V1(t)}E{2θV1(t)}+2θxT(t)Px(t)+2xT(t)P˙x(t)+(tσ+ht)˙xT(t)Q˙x(t)e2θhttσ˙xT(s)Q˙x(s)ds+(tσ+ht)[˙xT(t)S{U1}x(t)+2˙xT(t)(U1+U2)x(tσ)]+[2θ(tσ+ht)1]×[xT(t)S{U1}2x(t)+2xT(t)(U1+U2)x(tσ)+xT(tσ)S{U12U2}x(tσ)]. (3.8)

    Denote

    ϕ(t)=1ttσttσ˙x(s)ds,

    where the case that ϕ(t)|t=tσ can be understood as limttσϕ(t)=˙x(tσ) [41]. Then, utilizing Lemma 2.2 gives

    ttσ˙x(s)Q˙x(s)ds(ttσ)ϕT(t)Qϕ(t). (3.9)

    Furthermore, using the Newton-Leibniz formula and the expectation of (2.6), we can write

    0=2[xT(t)WT1+˙xT(t)WT2+xT(tσ)WT3]×[x(t)+x(tσ)+(ttσ)ϕ(t)], (3.10)
    0=2[xT(t)NT1,1+˙xT(t)NT2,1][˙x(t)+Ax(t)+BK(α+Δα(t))x(t)+BK(1αΔα(t))d(x(t))(ttσ)BK(α+Δα(t))ϕ(t)]. (3.11)

    Setting ς1(t)=col{x(t),˙x(t),x(tσ),d(x(t))}, ς2(t)= col{x(t),˙x(t),x(tσ),d(x(t)),ϕ(t)}, by employing Lemma 2.3, for positive definite matrices Λ1 and Λ2, we can write the following inequalities:

    2xT(t)NT1,1BKΔα(t)[x(t)d(x(t))(ttσ)ϕ(t)]=2xT(t)NT1,1BKΔα(t)[tσ+hthR1ς1(t)+ttσhR2ς2(t)]tσ+hthˉα2ςT1(t)RT1Λ1R1ς1(t)+ttσhˉα2ςT2(t)RT2Λ1R2ς2(t)+xT(t)NT1,1BKΛ11KTBTN1,1x(t). (3.12)

    Similarly,

    2˙xT(t)NT2,1BKΔα(t)[x(t)d(x(t))(ttσ)ϕ(t)]tσ+hthˉα2ςT1(t)RT1Λ2R1ς1(t)+ttσhˉα2ςT2(t)RT2Λ2R2ς2(t)+˙xT(t)NT2,1BKΛ12KTBTN2,1˙x(t). (3.13)

    Moreover, by using Assumption 1, we can derive

    xT(t)DTDx(t)dT(x(t))d(x(t))0. (3.14)

    By using inequalities (3.8)–(3.14), we have

    E{˙V1(t)}+2θE{V1(t)}tσ+hthςT1(t)Ω11ς1(t)+ttσhςT2(t)Ω21ς2(t)+xT(t)NT1,1BKΛ11KTBTN1,1x(t)+˙xT(t)NT2,1BKΛ12KTBTN2,1˙x(t),

    which, in conjunction with the Schur complement, Σ1<0, and Σ2<0, guarantees that

    E{˙V1(t)}+2θE{V1(t)}0. (3.15)

    Denote ξ3(t)=col{x(t),˙x(t),e(t),d(x(t))}. Then, in a similar way to the above proof, it is not hard to obtain

    E{˙V2(t)}2θE{V2(t)}+2θxT(t)Px(t)+xT(t)DTDx(t)+2xT(t)P˙x(t)+2[xT(t)NT1,2+˙xT(t)NT2,2]×[˙x(t)+Ax(t)+BK(α+Δα(t))x(t)+BK(1αΔα(t))d(x(t))+BK(α+Δα(t))e(t)]+ε1xT(t)Mx(t)+ε2[e(t)+x(t)]TM[e(t)+x(t)]eT(t)Me(t)dT(x(t))d(x(t)). (3.16)

    By using Lemma 2.3, for given positive definite matrices Λ3 and Λ4, we can obtain

    2xT(t)NT1,2BKΔα(t)ς3(t)xT(t)NT1,2BKΛ13KTBTN1,2x(t)+ˉα2ςT3(t)RT3Λ3R3ς3(t), (3.17)
    2˙xT(t)NT2,2BKΔα(t)(t)ς3(t)˙xT(t)NT2,2BKΛ14KTBTN2,2˙x(t)+ˉα2ςT3(t)RT3Λ4R3ς3(t). (3.18)

    Combining (3.16)–(3.18), we get

    E{˙V2(t)}+2θE{V2(t)}ςT3(t)Σ3ς3(t)+xT(t)NT1,2BKΛ13KTBTN1,2x(t)+˙xT(t)NT2,2BKΛ14KTBTN2,2˙x(t)

    for any tT2σ, which together with the Schur complement and Ω31<0, implies that

    E{˙V2(t)}+2θE{V2(t)}0. (3.19)

    According to the expression of V(t), it is easy to obtain that

    Vq(tσ)=Vu(tσ)=0,limt(tσ+h)Vq(t)=limt(tσ+h)Vu(t)=0,

    which confirms the continuity of V(t) at instants tσ and tσ+h.

    Then combining (3.15) and (3.19), for tT1σ we can derive

    E{V(t)}E{V(tσ)}e2θ(ttσ)E{V(tσ1)}e2θ(ttσ1)E{V(0)}e2θt.

    Similarly, for tT2σ, the same results can be obtained. In the light of (3.1), we get

    E{V(0)}λM(P)E{x(0)2}.

    It can be concluded that for any tT1σT2σ,

    E{V(t)}e2θtE{V(0)},

    which, together with (3.7), gives

    E{x(t)}min{λm(P),λm(Σ)}λM(P)eθtE{x0}.

    This completes the proof.

    When there is no deception attacks, d(x(t))=0, and system (2.6) becomes

    {˙x(t)=(A+BK)x(t)BKttσ˙x(s)ds,tT1σ,˙x(t)=(A+BK)x(t)+BKe(t),tT2σ, (3.20)

    and we can write the following sufficient condition:

    Corollary 1. For given feedback gain matrix K and positive scalars ε1, ε2, h, θ, under the NSETS (2.4), system (3.20) is exponentially stable, if there exist symmetric matrices P>0, Q>0, M>0, and general matrices U1, U2, W1, W2, W3, N1,1, N2,1, N1,2, N2,2, such that (3.2) and the following LMIs

    [Ω11,1Ω11,2Ω11,3Ω12,2Ω12,3Ω13,3]<0, (3.21)
    [Ω21,1Ω21,2Ω21,3Ω1,5S{N2,1}WT2Ω2,5Ω23,3hWT3Ω5,5]<0, (3.22)
    [Ω31,1Ω31,2Ω31,3S{N2,2}Ω32,3Ω33,3]<0 (3.23)

    hold true, where matrix blocks such as Ω11,1, Ω11,2, and Ω11,3 are the same as those in Theorem 3.1 except that α=1 and ˉα=0.

    In this section, we will explore the feasibility of the event-triggered controller design. On the basis of Theorem 3.1, the trigger matrix M and feedback-gain matrix K can be derived from the following result:

    Theorem 3.2. For given positive scalars ε1, ε2, h, θ, α, ˉα, ϑ and matrix D, under the NSETS (2.4) and random deception attacks, switched system (2.6) with control gain K=X1Y is MSE stable, if there exist symmetric matrices P>0, Q>0, U>0, M>0, ˉΛi>0 (i{1,2,3,4}), and general matrices X, Y, ˜W1, ˜W2, ˜W3, ˜N1,1, ˜N2,1, ˜N1,2, ˜N2,2, such that (3.2) and the following LMIs

    [˜Ω11˜Ω12˜Ω3]<0, (3.24)
    [˜Ω21˜Ω22˜Ω3]<0, (3.25)
    [˜Ω31˜Ω32˜Ω3]<0 (3.26)

    hold true, where

    ˜Ω11=[˜Ω11˜Ω12˜Ω13],˜Ω21=[˜Ω21˜Ω22˜Ω13],˜Ω31=[˜Ω31˜Ω32˜Ω33],˜Ω11=[˜Ω11,1˜Ω11,2˜Ω11,3˜Ω1,4˜Ω12,2˜Ω12,3˜Ω2,4˜Ω13,30I]+ˉα2˜RT1(˜Λ1+˜Λ2)˜R1,˜R1=[I00I],˜Ω21=[˜Ω21,1˜Ω21,2˜Ω21,3˜Ω1,4˜Ω1,5˜Ω22,2˜WT2˜Ω2,4˜Ω2,5˜Ω23,30h˜WT3I0he2θhQ]+ˉα2˜RT2(˜Λ1+˜Λ2)˜R2,˜R2=[I00IhI], ˜R3=[I0II],˜Ω31=[˜Ω31,1˜Ω31,2˜Ω31,3˜Ω31,4S{˜N2,2}αGTY˜Ω32,4(ε21)M0I]+ˉα2˜RT3(˜Λ3+˜Λ4)˜R3,˜Ω12=[YTG0000YTG00]T,˜Ω22=[˜Ω120],˜Ω32=[YTG0000YTG00]T,˜Ω13=[˜Λ100˜Λ2],˜Ω33=[˜Λ300˜Λ4],˜Ω3=ϑ[X00X]ϑ[XT00XT],˜Ω12=[˜ΩT1,800(1α)ϑYϑY0αϑY˜ΩT2,90(1α)ϑY0ϑY]T,˜Ω22=[˜ΩT1,800(1α)ϑYαϑhYϑY0αϑY˜ΩT2,90(1α)ϑYαϑhY0ϑY]T,˜Ω32=[˜Ω3T1,80αϑY(1α)ϑYϑY0αϑY˜Ω3T2,9αϑY(1α)ϑY0ϑY]T,˜Ω11,1=2θP+DTD+S{˜NT1,1A˜W1+(2θh1)U12+αGTY},˜Ω21,1=˜Ω11,1θhS{U1},˜Ω11,2=P˜W2˜NT1,1+h2S{U1}+AT˜N2,1,˜Ω21,2=˜Ω11,2h2S{U1},˜Ω11,3=˜WT1˜W3+(12θh)(U1U2), ˜Ω21,3=˜Ω11,3+2θh(U1U2),˜Ω1,4=(1α)GTY, ˜Ω1,5=h(˜WT1αGTY),˜Ω1,8=˜NT1,1BGTX+αϑYT, ˜Ω12,2=hQS{˜N2,1},˜Ω22,2=S{˜N2,1}, ˜Ω12,3=˜WT2h(U1U2),˜Ω2,4=(1α)GTY,˜Ω2,5=h(˜WT2αGTY), ˜Ω2,9=˜NT2,1BGTX,˜Ω13,3=S{˜W3+(2θh1)(U12U2)},˜Ω23,3=˜Ω13,3S{2θh(U12U2)},˜Ω31,1=2θP+S{˜NT1,2A+αGTY} +(ε1+ε2)M+DTD,˜Ω31,2=P˜NT1,2+AT˜N2,2+αYTG,˜Ω31,3=ε2M+αGTY,˜Ω31,4=(1α)GTY, ˜Ω31,8=˜NT1,2BGTX+αϑYT,˜Ω32,4=(1α)GTY, ˜Ω32,9=˜NT2,2BGTX,

    and G is an all-ones matrix with the same dimension as B.

    Proof. By Lemma 2.4, it follows from (3.24) that

    ˜Ω11+S{[˜NT1,1BGTL00˜NT2,1BGTL00000000][X00X]1[αY00(1α)YY0αY00(1α)Y0Y]}<0,

    which can be equivalently rewritten as

    [Ω11Ω12Ω3]<0.

    In other words, we can derive (3.3) from (3.24). Similarly, (3.4)–(3.5) can be obtained from (3.25)–(3.26). The proof is complete.

    Corollary 2. For given positive scalars ε1, ε2, h, θ, ϑ, under the NSETS (2.4), switched system (3.20) with control gain K=X1Y is exponentially stable, if there exist symmetric matrices P>0, Q>0, U>0, M>0, and general matrices X, Y, ˜W1, ˜W2, ˜W3, ˜N1,1, ˜N2,1, ˜N1,2, ˜N2,2, such that (3.2) and the following LMIs

    [˜Ω11,1˜Ω11,2˜Ω11,3˜Ω1,8˜Ω12,2˜Ω12,3˜Ω2,9˜Ω13,30ϑS{X}]<0, (3.27)
    [˜Ω21,1˜Ω21,2˜Ω21,3˜Ω1,5˜Ω1,8˜Ω22,2˜WT2˜Ω2,5˜Ω2,9˜Ω23,3h˜WT30he2θhQhϑYTϑS{X}]<0, (3.28)
    [˜Ω31,1˜Ω31,2˜Ω31,3˜Ω31,8S{˜N2,2}GTY˜Ω32,9(ε21)M0ϑS{X}]<0 (3.29)

    hold true, where matrix blocks such as ˜Ω11,1, ˜Ω11,2, and ˜Ω11,3 are the same as those in Theorem 3.2 except that α=1 and ˉα=0.

    In this section, we use a simplified inverted pendulum model [42] to illustrate the validity of the proposed NSETS-based controller design scheme under random deception attacks. This model can be described as follows:

    ˙x(t)=[1.840.337.181.14]x(t)+[2.430.42]u(t).

    First, we will co-design the triggering matrix W and control gain K to guarantee the MSE stability of the above system.

    Choosing triggering thresholds ε1=ε2=0.1, waiting interval h=0.05, deception attack signal d(x(t))=tanh(0.15x(t)), uncertain probability term Δα(t)=αpsin(t), and ϑ=0.01. Then, based on Theorem 3.2, when α=0.8, for different probability uncertainty coefficient αp, the maximum exponential decay rate θmax can be obtained, which is listed in Table 1. It is straightforward to see that, as the probability uncertainty coefficient grows, the maximum exponential decay rate continues to deteriorate. In addition, Table 2 gives the values of θmax under the different probability α of deception attacks occurrence (when αp=0.1). It can be found that as the value of α decreases (which means the frequency of deception attacks increases), the performance of the controller declines.

    Table 1.  Maximum exponential decay rate θmax under different probability uncertainty coefficient αp.
    αp 0.06 0.07 0.08 0.09 0.1
    θmax 0.5843 0.5459 0.5080 0.4708 0.4341

     | Show Table
    DownLoad: CSV
    Table 2.  Maximum exponential decay rate θmax under different estimated probability α.
    α 0.88 0.86 0.84 0.82 0.8
    θmax 0.4655 0.4613 0.4549 0.4459 0.4341

     | Show Table
    DownLoad: CSV

    Next, to further reflect on the impact of deception attacks, we consider two cases: with and without deception attacks. In Case 1, the estimated probability of deception attacks and uncertainty coefficient are fixed as α=0.5, αp=0.08, and other parameters are unchanged. By solving the LMIs in Theorem 3.2, the matrices M and K can be computed as

    M=[0.78930.01580.01580.4791],K=[0.06950.3175].

    In Case 2, α=1 and αp=0 (that is, the probability of occurring deception attacks is 0). By solving the LMIs in Corollary 2, the matrices M and K are calculated as

    M=[1.23330.41990.41991.3964],K=[0.18410.2688].

    In the simulations, the initial condition is taken as x0=[1,1]T, and the running time is set as 10s. Figures 13 depict the state trajectories, control inputs, and release moment intervals between any two successively release moments of Cases 1 and 2, respectively. It can be seen that the state variables can converge to zero faster in the absence of deception attacks, which is consistent with the information in Table 2.

    Figure 1.  State trajectories.
    Figure 2.  Control inputs.
    Figure 3.  Release moment intervals.

    Finally, we will show the effect of triggering thresholds ε1 and ε2 in NSETS on the number of transmitted signals. Let tn and tr denote the number of trigger times and the ratio of transmitted signals, respectively, and other parameters are the same as those in Case 1. Then, as shown in Table 3, when ε1 and ε2 are both set to 0, the NSETS degenerates into the TTS in [16], and tn is as high as 200; when ε1=0.1 and ε2=0, the ETS changes into the SETS in [31], and tn is reduced to 71; when ε1 is fixed, tn will continue to decrease as ε2 increases. It can be observed that compared with SETS, the tr of the NSETS decreases by more than 8.5%, and compared with TTS, the rate can be reduced by more than 73%. These results demonstrate that the addition of trigger condition xT(tσ)Mx(tσ) significantly conserves network resources while ensuring stability.

    Table 3.  Comparison of trigger times under different triggering thresholds.
    Scheme TTS in [16] SETS in [31] NSETS in this paper
    (ε1,ε2) (0,0) (0.1,0) (0.1,0.05) (0.1,0.1) (0.1,0.15) (0.1,0.2)
    tn 200 71 54 43 36 32
    tr 100% 35.5% 27% 21.5% 18% 16%

     | Show Table
    DownLoad: CSV

    In this paper, the event-triggered exponential stabilization of NCSs under deception attacks has been investigated. Unlike existing ETSs, an NSETS, which additionally introduces a prescribed trigger term regarding the last triggering moment (i.e., ε2xT(tσ)Mx(tσ)), has been designed in (2.4). It has been demonstrated that the additional trigger term plays a positive role in reducing the number of trigger times. Moreover, by using a Bernoulli variable with uncertain probability to characterize the randomness of deception attacks, a new mathematical model of random deception attacks has been constructed in (2.3). A piecewise-defined Lyapunov function, which can make use of the system information at the instants tσ and tσ+h, has been established, which allows us to establish a sufficient exponential stability conation. Based on this, a co-design of the trigger and feedback-gain matrices under NSETS has been derived in terms of LMIs. Finally, a simulation example has been given to confirm the effectiveness of the developed design method. Future attention will be focused on the event-triggered security control problem of time-delay Markov jump systems [43,44,45,46].

    This work was supported by the Key Research and Development Project of Anhui Province (Grant No. 202004a07020028).

    The authors declare there is no conflict of interest.



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