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Research article

Event-triggered bipartite consensus of multi-agent systems in signed networks

  • Received: 17 August 2021 Revised: 19 December 2021 Accepted: 27 December 2021 Published: 07 January 2022
  • MSC : 93C57, 93C65

  • This paper focuses on the event-triggered bipartite consensus of multi-agent systems in signed networks, where the dynamics of each agent is assumed to be Lur'e system, and both the cooperative interaction and antagonistic interaction are allowed among neighbor agents. A novel event-triggered communication scheme is presented to save limited network resources, and distributed bipartite control techniques are raised to address the bipartite leaderless consensus and bipartite leader-following consensus respectively. By virtue of the Lyapunov stability theory and algebraic graph theory, bipartite consensus conditions are derived, which can be easily solved by MATLAB. In addition, the upper bounds of the sampling period and triggered parameter can be estimated. Finally, two examples are employed to show the validity and advantage of the proposed transmission scheme.

    Citation: Hongjie Li. Event-triggered bipartite consensus of multi-agent systems in signed networks[J]. AIMS Mathematics, 2022, 7(4): 5499-5526. doi: 10.3934/math.2022305

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  • This paper focuses on the event-triggered bipartite consensus of multi-agent systems in signed networks, where the dynamics of each agent is assumed to be Lur'e system, and both the cooperative interaction and antagonistic interaction are allowed among neighbor agents. A novel event-triggered communication scheme is presented to save limited network resources, and distributed bipartite control techniques are raised to address the bipartite leaderless consensus and bipartite leader-following consensus respectively. By virtue of the Lyapunov stability theory and algebraic graph theory, bipartite consensus conditions are derived, which can be easily solved by MATLAB. In addition, the upper bounds of the sampling period and triggered parameter can be estimated. Finally, two examples are employed to show the validity and advantage of the proposed transmission scheme.



    During the past decade, the cooperative control of multi-agent systems has got compelling attentions owing to intensive applications [1,2,3]. As one of the most important topics of cooperative behaviors, the consensus problem of multi-agent systems has attracted much attention, many fruitful and crucial results have been obtained to build the theoretical consensus framework, see [4,5,6,7,8,9,10,11,12,13,14,15] and the references therein.

    Most of the existing results are focused on the cooperative systems, however, the competitive relationship is very common in practical applications[16,17]. Therefore, more and more attention has been paid to signed networks with both cooperative and antagonistic links[18,19,20,21,22]. In order to achieve bipartite consensus, the structurally balanced condition is essential [23]. As an extension to the bipartite consensus, the concept of interval bipartite is introduced in [24], and interval bipartite consensus results are derived for structurally balanced networks and structurally unbalanced networks. If the network structure and node function satisfy some given conditions, the synchronization conditions are established for first-order multi-agent systems by utilizing pinning control strategy[25,26], the above results are extended to second-order nonlinear multi-agent systems[27,28], where the event-triggered control algorithm and pinning control strategy are used to achieve bipartite consensus. For general linear multi-agent systems, a systematical approach is given in [29] for solving the bipartite consensus problem. In [30], the bipartite tracking consensus problem is investigated for a dynamic leader. The finite-time bipartite consensus is studied in [31]. The adaptive bipartite consensus control is designed for high-order multi-agent systems [32]. Additionally, similar to the bipartite consensus problem, the reverse group consensus is firstly studied in the cooperation-competition network [33], the results show that the couple group consensus can be achieved if the mirror graph is strongly connected, as an extension, the adaptive robust bipartite consensus is considered for high-order uncertain multi-agent systems over the cooperation-competition network[34].

    In practical multi-agent systems, each agent is equipped with a micro-processor, it can collect information from neighboring agents, therefore, it is desired to implement the controller updates on a digital platform [35], the scheduling can be done in a time-triggered scheme or an event-triggered scheme, where the time-triggered scheme has a fixed sampling period, which should be selected to guarantee a desired performance under worst conditions, this kind of triggered method will send many unnecessary sampling data to neighboring agents. Considering the limited network resources, the event-triggered scheme is an effective method to improve the resources utilization while ensuring a satisfactory performance [36,37,38].

    The design of event-triggered transmission scheme is crucial to determine whether the sampling data should be transmitted. The event-triggered transmission schemes can be classified into two types, the first case is the absolute error-based event-triggered transmission scheme [38], which is dependent on a pre-specified positive instant and the state error. The second case is the relative error-based event-triggered transmission scheme, which is related to the system state [39,40], these schemes can reduce the data transmission significantly compared with the periodical transmission scheme. More recently, significant considerations have been focused on the event-triggered control for multi-agent systems [41,42,43,44,45,46,47,48,49,50,51]. Actuator saturation can be observed in many practical systems due to physical or safety constraints, two kinds of distributed event-triggered control schemes are designed for the consensus of nonlinear systems subject to input saturation [41], where event-triggered parameters can be adaptively adjusted by using some adaptive laws. In [42], the distributed protocol is assumed to be subject to saturation, by utilizing the event-triggered control, the group consensus is discussed for a class of multi-agent systems with non-identical dynamics. The general linear models are considered to investigate event-triggered consensus problems in [43], the advantage of the event-based strategy is the significant decrease of the number of controller updates. Three types of event-triggered schemes are proposed for different network topologies [44]. Noted that communication topologies face more risks from cyber attacks, some distributed event-triggered schemes are proposed for linear and nonlinear multi-agent systems with Dos attacks[45,46]. Based on event-triggered schemes, the consensus problems are discussed for nonlinear systems [47,48]. Especially, in [48], an integrated sampled-data-based event-triggered communication scheme includes advantages of both absolute and relative error-based event-triggered transmission schemes, thus it can lead to a high efficiency of data transmissions. In [49], considering quantized information, the periodic event-triggered algorithms are proposed for the consensus of multi-agent systems. The results on observer-based event-triggered consensus are also developed in [51,52]. In addition, switching topologies and stochastic sampling have been also considered for the consensus of multi-agent systems by event-triggered schemes[53,54,55,56], where the stochastic sampling interval randomly switches between two given values. The dynamic event-triggered schemes are developed for the consensus of multi-agent systems [57,58], compared with the traditional static event-triggered schemes, the time-varying threshold can ensure less triggered instants. In [59], the new event-triggered control law and triggering condition are constructed without continuous inter-neighboring communication, the event-triggered control is designed for the prescribed-time bipartite consensus. In [60], a novel observer-based bipartite control scheme is developed on the basis of two event-triggering mechanisms. In [61], a mode-dependent event-triggered transmission strategy is proposed for fixed-time bipartite consensus of a class of nonlinear multi-agent systems. In [62], distributed event-triggered control strategy is proposed for bipartite consensus for high-order multi-agent systems. However, the above schemes still embrace some room for improvement, in [39,40,45,46], the next transmission instant is determined by tk+1h=tkh+min{eT(jkh)Φe(jkh)>σxT(tkh)Φx(tkh)}, where Φ is a symmetric positive definite matrix, which can be replaced by tk+1h=tkh+min{eT(jkh)Φ1e(jkh)>σxT(tkh)Φ2x(tkh)}, where Φ1 and Φ2 are two symmetric positive definite matrices, in simulation examples, it can be seen that this scheme can lead to larger sampling period. Compared with [27,52,55], in our paper the form of the event-triggered scheme is relatively simple and easy to be applied in practical applications, the lower bound event interval is the sampling period h, which is strictly larger than zero, therefore, the Zeno behavior is excluded in our proposed scheme. Moreover, in [27,52,55], in order to avoid Zeno behavior, the index term βeγ(tt0) has been introduced in the event-triggered function, which may bring some trouble in the theoretical analysis or practical application. In addition, the bipartite synchronization of Lur'e network or neural network have been investigated in[25,26], where the controller designs have been based on the continuous or sampled communication information among the agents. In[63], the consensus problem is discussed for second-order multi-agent systems based on the event-triggered scheme, but the consensus protocol contains the continuous communication information of the leader. To the best of our knowledge, there are few results focusing on bipartite consensus of multi-agent systems with positive and negative communication links via event-triggered strategy, which is the main motivation of this paper.

    Motivated by the aforementioned discussion, this paper is devoted to investigating bipartite consensus of Lur'e system in signed networks. The main contributions of this paper can be summarized as follows: (1) Compared with existing communication schemes in the literatures, the proposed event-triggered scheme can ensure the desired performance while further reducing the frequency of data transmission. (2) By using coordinate transform and matrix analysis techniques, sufficient conditions are derived to guarantee that bipartite leaderless consensus and leader-following consensus can be achieved, respectively. (3) The upper bound of the sampling period can be obtained by solving a constrained optimization problem. (4) In the proposed event-triggered communication scheme, the Zeno behavior is excluded owing to the fact that the lower bound of inter event interval is the sampling period.

    Notation: Rn denotes the n-dimensional Euclidean space and Rn×m is a set of real n×m matrices. diag{} stands for a block-diagonal matrix. The notation with the superscript 1n(0n) indicates the n-dimensional column vector with each entry being 1(0). sgn() is the standard sign function. Notations . and denote the Euclidian norm and the Kronecker product, respectively.

    The communication topology of a network can be modeled as a signed graph g=(v,ε,A), where v=(v1,v2,,vN) denotes the set of nodes, εv×v is the set of edges, A=(aij)N×N denotes a signed adjacency matrix of g representing the communication topology. If aij=0, it means that agent i cannot receive information from agent j. The entry aij can be allowed to be positive or negative, with aij>0 indicating that node i and node j are cooperative, aij<0 indicating the node i and node j are competitive. Based on the weighted adjacency matrix A, the Laplacian matrix of the signed graph g can be defined as L=(lij)N×N, where lii=k=1,ki|aik|, lij=aij(ij). If there exists a rooted node having directed path to all other nodes, the signed graph g contains a directed spanning tree.

    Remark 1. For the above definition of Laplacian matrix L, the sum of the ith row of L is Nk=1,ki|aik|Nk=1,kiaik, the element aij can be allowed to be positive or negative for the signed graph g, thus Nk=1,ki|aik|=Nk=1,kiaik may not be hold, that is the Laplacian matrix of signed digraph may not be a zero-row-sum matrix, which leads to the consequence that the traditional control protocol design method can not be used directly.

    Consider a class of nonlinear multi-agent systems with the Lur'e system form

    ˙xi(t)=Axi(t)+Bf(Cxi(t))+ui(t),(i=1,2,,N), (2.1)

    where xi(t)Rn is the state vector of node i, f(Cxi(t))=[f1(CT1xi(t)),f2(CT2xi(t)),,fm(CTmxi(t))]TRm is a vector-valued nonlinear function. ARn×n, BRn×m, C=[C1,C2,,Ci,,Cm]TRm×n and Ci=[Ci1,Ci2,,Cin]T(i=1,2,,m) are some constant matrices. ui(t)Rn is the control input.

    Assumption 1. Suppose that the nonlinear function fk()(k=1,2,,m) is an odd functionsatisfying the following sector condition

    0fk(z2)fk(z1)z2z1δk,z1,z2R,z1z2, (2.2)

    where δk>0(k=1,2,,m).

    Remark 2. The nonlinear function f() is assumed to be an odd function due to the fact that the gauge transformation can do work well, which can be found in the proof of Theorem 1. In addition, it is a mild assumption in existing literatures, whether the limitation of nonlinear function can be removed may be discussed in the future.

    Definition 1. [25] A signed graph g is called structurally balanced if the node set v can be divided into two sub-networks V1 and V2, which satisfy V1V2=v and V1V2=Φ, and aij0 holds for node i and node j which are in the same sub-network, and aij<0 holds for node i and node j which are in different sub-networks. Otherwise, the signed graph g is called structurally unbalanced.

    Lemma 1. [23]Suppose that the signed network g is structurally balanced, if and only if there is a gauge transformationW such that WTAW=ˉA with nonnegative entries, where W=diag{w1,w2,,wN} with wi{1,1}, A=(aij)N×N and ˉA=(|aij|)N×N are the adjacency matrices ofthe signed and unsigned networks, respectively. In addition, W provides a partition, that is V1={vi|wi=1} and V2={vi|wi=1}.

    Lemma 2. [64]If x(t):(a,b)Rn is an absolutely continuous function satisfying x(a)=0 and MRn×n is a positive definite matrix, then the following Wirtinger's inequality holds

    baxT(s)Mx(s)ds4(ba)2π2ba˙xT(s)M˙x(s)ds. (2.3)

    Lemma 3. [65]For any positive definite matrix MRn×n and a vector function x(t):(a,b)Rn, such that the following integrations are well defined, then

    (ba)baxT(s)Mx(s)dsbaxT(s)dsMbax(s)ds. (2.4)

    Lemma 4. [66] For any two real vector functions y1(t)Rn and y2(t)Rn, a positive definite matrix RRn×n and any matrix SRn×n, then

    1αyT1(t)Ry1(t)+11αyT2(t)Ry2(t)yT(t)[RSTSR]y(t), (2.5)

    where y(t)=[y1(t)y2(t)], [RSTSR]>0 and 0<α<1.

    Lemma 5. [67] The following linear matrix inequality

    [Q(x)S(x)ST(x)R(x)]>0, (2.6)

    where Q(x)=QT(x), R(x)=RT(x), is equivalent to either of the following conditions

    (1)Q(x)>0,R(x)ST(x)Q1(x)S(x)>0,(2)R(x)>0,Q(x)S(x)R1(x)ST(x)>0.

    In this section, it is assumed that the topology is a directed signed graph and structurally balanced. An event-triggered scheme is designed, and by some transformation, the bipartite leaderless consensus problem is converted to the stability of the error system.

    Definition 2. [25] The signed Lur'e network (2.1) is said to achieve bipartite leaderless consensus if the following equation holds

    limtwixi(t)wjxj(t)=0,(i,j=1,2,,N;ij), (3.1)

    where wi=1, (iV1); wi=1, (iV2).

    Remark 3. If the agents i,jV1 or i,jV2, which means they are in the same sub-network, then wi=wj=1 or wi=wj=1, the equation (3.1) reduces to limtxi(t)xj(t)=0, which is the traditional leaderless consensus of unsigned networks. If the agents iV1,jV2 or iV2,jV1, it means that the agents i and j are in different sub-networks, the equation (3.1) reduces to limtxi(t)+xj(t)=0, which can be rewritten as limtxi(t)(xj(t))=0, which represents agent j competes against agent i.

    For reducing the communication burden among the agents, the following sampled control algorithm is designed

    ui(t)=cjN(i)|aij|[xi(tkh)sgn(aij)xj(tkh)],(i=1,2,,N;t[tkh,tk+1h);kZ+), (3.2)

    where c>0 is the coupling strength, tkh(k=0,1,2,) is the release times, t0h=0, {t0,t1,}{0,1,2,}, the data will be updated for t[tkh,tk+1h) until t=tk+1h, which can effectively reduce the data transmission among the agents. When aij>0, the coupling information from its neighboring agent can be described as aij[xi(tkh)xj(tkh)], which means agent i and agent j are cooperative, when aij<0, the coupling information from its neighboring agent is aij[xi(tkh)+xj(tkh)], which represents that interaction is antagonistic.

    Applying (3.2) to the system (2.1) yields

    ˙xi(t)=Axi(t)+Bf(Cxi(t))cjN(i)|aij|[xi(tkh)sgn(aij)xj(tkh)]. (3.3)

    Based on the definition of the Laplacian matrix of signed graph, the equation (3.3) can be rewritten as

    ˙xi(t)=Axi(t)+Bf(Cxi(t))cNj=1lijxj(tkh),(i=1,2,,N), (3.4)

    where lii=k=1,ki|aik|, lij=aij(ij).

    Considering limited bandwidth of the communication network, to further reduce the data transmission among the agents, an event-triggered communication scheme is proposed for system (2.1), which is utilized to decide whether the current sampled data should be transmitted to neighboring agents or not. Supposed that the latest transmitted data is x(tkh), then the next transmission instant tk+1h can be determined by

    tk+1h=tkh+min{lh|[ei(tkh+lh)ei(tkh)]TΦ(1)i[ei(tkh+lh)ei(tkh)]>σieTi(tkh+lh)Φ(2)iei(tkh+lh)}, (3.5)

    where the error ei(t)=wixi(t)w1x1(t), σi>0 are event-triggered parameters, Φ(1)i and Φ(2)i, (i=2,3,,N) are positive definite event-triggered matrices which need to be designed. tk+1htkh denotes the release period which corresponds to the sampling period given by the event-triggered scheme in (3.5).

    Remark 4. Noted that the above event-triggered communication scheme (3.5) including the following special cases: (I) if Φ(1)i=Φ(2)i=Φi(i=2,3,,N), the scheme (3.5) shrinks to a discrete event-triggered transmission scheme [39,40]. (II) if σi=0 and tk+1h=tkh+h, then transmission scheme (3.5) shrinks to a periodic transmission scheme. (III) if Φ(1)i=Φ(2)i=In(i=2,3,,N), transmission scheme (3.5) becomes a discrete absolute error-based transmission scheme. Hence the proposed event-triggered communication scheme includes some existing schemes. The proposed transmission scheme (3.5) where only the sampled-data is used is different from a continuous absolute error-based scheme that needs to monitor the continuous measurement. Compared with [27], the form of the event-triggered scheme is relatively simple and easy to be applied in practical applications, moreover in our paper the lower bound event interval is the sampling period h, which is strictly larger than zero, therefore, the Zeno behavior is excluded in our proposed scheme.

    Remark 5. The bipartite synchronization of Lur'e network or neural network has been investigated in[25,26], where the controller designs have been based on the continuous or sampled communication information among the agents. This paper has been based on the event-triggered scheme, the control signals are only updated at a series of triggered instants. In [63], the consensus problem is discussed for second-order multi-agent systems based on event-triggered scheme, but the consensus protocol contains the continuous communication information of the leader.

    Let ˉxi(t)=wixi(t)(i=1,2,,N), here wi=1(iV1) and wi=1(iV2), it follows from (3.4) that

    ˙ˉxi(t)=Aˉxi(t)+Bwif(Cwiˉxi(t))cNj=1ˉlijˉxj(tkh),(i=1,2,,N), (3.6)

    where ˉL=(ˉlij)N×N is the Laplacian matrix of the unsigned graph with zero-row-sum, ˉlii=k=1,ki|aik|, ˉlij=wilijwj=|aij|(ij).

    By using Assumption 1, it is easy to obtain wif(Cwiˉxi(t))=w2if(Cˉxi(t))=f(Cˉxi(t)) owing to f() being an odd function, then (3.6) can be simplified as

    ˙ˉxi(t)=Aˉxi(t)+Bf(Cˉxi(t))cNj=1ˉlijˉxj(tkh),(i=1,2,,N). (3.7)

    In views of (3.3) and ei(t)=ˉxi(t)ˉx1(t)(i=2,3,,N), one has

    ˙ei(t)=Aei(t)+Bf(Cˉxi(t))Bf(Cˉx1(t))cNj=2(ˉlijˉl1j)ej(tkh),(i=2,3,,N). (3.8)

    Partly inspired by [39,40], the interval [tkh,tk+1h) can be expressed as the union of several subintervals

    [tkh,tk+1h)=tk+11l=0[tkh+lh,tkh+lh+h). (3.9)

    Define τ(t)=t(tkh+lh), for t[tkh+lh,tkh+lh+h), it can be derived that 0τ(t)<h, let ˉei(tkh+lh)=ei(tkh+lh)ei(tkh)(i=2,3,,N), we have

    ei(tkh)=ei(tτ(t))ˉei(tkh+lh). (3.10)

    Substituting (3.10) into (3.8) yields

    ˙ei(t)=Aei(t)+Bf(Cˉxi(t))Bf(Cˉx1(t))cNj=2(ˉlijˉl1j)[ej(tτ(t))ˉej(tkh+lh)]. (3.11)

    By rewrite (3.11) in compact matrix form, one may further get that

    ˙e(t)=(IN1A)e(t)c(ˆLIn)e(tτ(t))+c(ˆLIn)ˉe(tkh+lh)+(IN1B)η(t), (3.12)

    where

    e(t)=[eT2(t),eT3(t),,eTN(t)]T,η(t)=[ηT2(t),ηT3(t),,ηTN(t)]T,ηi(t)=[ηi1(t),ηi2(t),,ηim(t)]T,ηik(t)=fk(CTkˉxi(t))fk(CTkˉx1(t))(k=1,2,,m),ˆL=(ˆlpq)R(N1)×(N1),ˆlpq=ˉl(p+1),(q+1)ˉl1,(q+1)(p,q=1,2,,N1).

    Combining (3.5) and (3.10), for t[tkh,tk+1h), the current sampled data ei(tkh+lh) will not be sent, then the event-triggered scheme can be rewritten as

    [ei(tkh+lh)ei(tkh)]TΦ(1)i[ei(tkh+lh)ei(tkh)]σieTi(tτ(t))Φ(2)iei(tτ(t)). (3.13)

    By using Kronecher product, from (3.13), one get

    ˉeT(tkh+lh)Φ(1)ˉe(tkh+lh)eT(tτ(t))(σIn)Φ(2)e(tτ(t)), (3.14)

    where

    ˉe(tkh+lh)=[ˉeT2(tkh+lh),ˉeT3(tkh+lh),,ˉeTN(tkh+lh)]T,ˉei(tkh+lh)=ei(tkh+lh)ei(tkh),Φ(1)=diag{Φ(1)2,Φ(1)3,,Φ(1)N},Φ(2)=diag{Φ(2)2,Φ(2)3,,Φ(2)N},σ=diag{σ2,σ3,,σN}.

    Theorem 1. Suppose that Assumption 1 holds, for given sampled period h>0, trigger parameters σi>0(i=2,3,,N) and coupled strength c>0, the bipartite leaderless consensus can be reached in system (2.1)under the control law (3.2) and event-triggered scheme (3.14), if there exist some (N1)n×(N1)n positivedefinite matrices P>0, Q>0, R>0, Ω>0 and T=diag{τ1,τ2,,τm}>0, and matrix UR(N1)n×(N1)n, such that the following linear matrix inequalities hold

    [Γ11Γ12UTcP(ˆLIn)π24ΩΓ16h(IN1A)TRh(IN1A)TΩΓ22UT+R000hc(ˆLIn)TRhc(ˆLIn)TΩQR00000Φ(1)00hc(ˆLIn)TRhc(ˆLIn)TΩπ24Ω000IN1Th(IN1B)TRh(IN1B)TΩR0Ω]<0, (3.15)
    [RUTR]>0, (3.16)

    where

    Γ11=P(IN1A)+(IN1A)TP+QRπ24Ω,Γ12=cP(ˆLIn)UT+R,Γ16=P(IN1B)+IN1CTΔT,Γ22=2R+U+UT+(σIn)Φ(2),Δ=diag{δ1,δ2,,δm}.

    Proof. Construct the following Lyapunov function candidate

    V(t)=4i=1Vi(t), (3.17)

    where

    V1(t)=eT(t)Pe(t),V2(t)=ttheT(s)Qe(s)ds,V3(t)=htthts˙eT(v)R˙e(v)dvds,V4(t)=h2ttkh+lh˙eT(s)Ω˙e(s)dsπ24ttkh+lh[e(s)e(tkh+lh)]TΩ[e(s)e(tkh+lh)]ds,

    where P,Q,R,ΩR(N1)n×(N1)n>0.

    Next, some necessary explanations are given to show the Lyapunov function V4(t) is valid. By using the Wirtinger's inequality in Lemma 2, one has

    h2ttkh+lh˙eT(s)Ω˙e(s)ds=h2ttkh+lh[˙e(s)˙e(tkh+lh)]TΩ[˙e(s)˙e(tkh+lh)]dsh2π24[t(tkh+lh)]2ttkh+lh[e(s)e(tkh+lh)]TΩ[e(s)e(tkh+lh)]dsπ24ttkh+lh[e(s)e(tkh+lh)]TΩ[e(s)e(tkh+lh)]ds, (3.18)

    it can be easily derived that V4(t)0, thus the Lyapunov function V4(t) is valid.

    By using Assumption 1, we can obtain

    ηik(t)(ηik(t)δkCTkei(t))0, (3.19)

    for τk>0(k=1,2,,m), we have

    mk=1τkηik(t)(ηik(t)δkCTkei(t))0. (3.20)

    The above equation (3.20) can be rewritten in the compact matrix form as follows

    ηT(t)(IN1T)η(t)eT(t)(IN1CTΔT)η(t)0, (3.21)

    where

    T=diag{τ1,τ2,,τm},C=[C1,C2,,Cm]T,Δ=diag{δ1,δ2,,δm}.

    For t[tkh,tk+1h), taking the derivative of Vi(t)(i=1,2,3,4) along the trajectories (3.12), one gets

    ˙V1(t)=2eT(t)P[(IN1A)e(t)c(ˆLIn)e(tτ(t))+c(ˆLIn)ˉe(tkh+lh)+(IN1B)η(t)]=eT(t)[P(IN1A)+(IN1A)TP]e(t)2ceT(t)P(ˆLIn)e(tτ(t))+2ceT(t)P(ˆLIn)ˉe(tkh+lh)+2eT(t)P(IN1B)η(t). (3.22)
    ˙V2(t)=eT(t)Qe(t)eT(th)Qe(th). (3.23)
    ˙V3(t)=h2˙eT(t)R˙e(t)htth˙eT(s)R˙e(s)ds. (3.24)
    ˙V4(t)=h2˙eT(t)Ω˙e(t)π24[e(t)e(tkh+lh)]TΩ[e(t)e(tkh+lh)]. (3.25)

    For any τ(t)[0,h), it follows from Jensen's inequality in Lemma 3 and the reciprocally convex approach in Lemma 4 that

    htth˙eT(s)R˙e(s)ds=httτ(t)˙eT(s)R˙e(s)dshtτ(t)th˙eT(s)R˙e(s)dshτ(t)[e(t)e(tτ(t))]TR[e(t)e(tτ(t))]hhτ(t)[e(tτ(t))e(th)]TR[e(tτ(t))e(th)][e(t)e(tτ(t))e(tτ(t))e(th)]T[RUTUR][e(t)e(tτ(t))e(tτ(t))e(th)]=[e(t)e(tτ(t))e(th)]T[I0II0I][RUTUR][II00II][e(t)e(tτ(t))e(th)]=[e(t)e(tτ(t))e(th)]T[RUTRUTR+U2RUUTUTRUURR][e(t)e(tτ(t))e(th)]. (3.26)

    It can be derived from (3.25) that

    π24[e(t)e(tkh+lh)]TΩ[e(t)e(tkh+lh)]=π24[e(t)e(tkh+lh)]T[ΩΩΩΩ][e(t)e(tkh+lh)]. (3.27)

    From (3.12), (3.22)–(3.27), we can obtain

    ˙V(t)eT(t)[P(IN1A)+(IN1A)TP]e(t)2ceT(t)P(ˆLIn)e(tτ(t))+2ceT(t)P(ˆLIn)ˉe(tkh+lh)+2eT(t)P(IN1B)η(t)+eT(t)Qe(t)eT(th)Qe(th)+h2˙eT(t)(R+Ω)˙e(t)[e(t)e(tτ(t))e(th)]T[RUTRUTR+U2RUUTUTRUURR][e(t)e(tτ(t))e(th)]π24[e(t)e(tkh+lh)]T[ΩΩΩΩ][e(t)e(tkh+lh)]ˉeT(tkh+lh)Φ(1)ˉe(tkh+lh)+eT(tτ(t))(σIn)Φ(2)e(tτ(t))ηT(t)(IN1T)η(t)+eT(t)(IN1CTΔT)η(t)=ξT(t)Σξ(t)+h2˙eT(t)(R+Ω)˙e(t)=ξT(t)[Σ+h2ΠT(R+Ω)Π]ξ(t), (3.28)

    where

    ξ(t)=[eT(t)eT(tτ(t))eT(th)ˉeT(tkh+lh)eT(tkh+lh)ηT(t)]T,Σ=[Γ11Γ12UTcP(ˆLIn)π24ΩΓ16Γ22UT+R000QR000Φ(1)00π24Ω0IN1T],Π=[INAc(ˆLIn)0c(ˆLIn)0INB],

    using Schur complement in Lemma 5 with (3.15), we can obtain Σ+h2ΠT(R+ΩΠ)<0, then

    ˙V(t)<0, (3.29)

    thus system (3.12) is globally asymptotically stable at the origin, then we have limtwixi(t)w1x1(t)=0(i=2,3,,N), which means limtwixi(t)wjxj(t)=0(i=2,3,,N;ij), by using Definition 1, it can be derived that bipartite leaderless consensus is achieved. This completes the proof.

    Remark 6. As a by-product, given the triggered parameters σi>0(i=2,3,,N) and the coupling strength c>0, the following constrained optimization problem can be employed to find the maximum allowable sampling period h, that is

    MaxhSubjectto:(3.15)and(3.16).

    Remark 7. In Theorem 1, the triggered parameters σi>0(i=2,3,,N) depend on the variation of the system state, if the parameters σi keep a big value, then the network resources can be further saved, for given the coupling strength c and the sampling period h, when Φ(1)=Φ(2), the maximum values of trigger parameters σi>0(i=2,3,,N) can be obtained by the following constrained optimization problem

    MaxσSubjectto:(3.15)and(3.16).

    Notice that if σi=0(i=2,3,,N), the event-triggered scheme will reduce to the time-triggered scheme. Let σi0+ in Theorem 1, we will obtain the bipartite leaderless consensus under the sampled-data control.

    Corollary 1. Suppose that Assumption 1 holds, for given sampled period h>0 and coupled strength c>0, the bipartite leaderless consensus under the sampled-data control can be reached in system (2.1)under the control law (3.2), if there exist some (N1)n×(N1)n positivedefinite matrices P>0, Q>0, R>0, Ω>0 and T=diag{τ1,τ2,,τm}>0, and matrix UR(N1)n×(N1)n, such that the following linear matrix inequalities hold

    [Γ11Γ12UTπ24ΩΓ16h(IN1A)TRh(IN1A)TΩˉΓ22UT+R00hc(ˆLIn)TRhc(ˆLIn)TΩQR0000π24Ω000IN1Th(IN1B)TRh(IN1B)TΩR0Ω]<0, (3.30)
    [RUTR]>0, (3.31)

    where

    Γ11=P(IN1A)+(IN1A)TP+QRπ24Ω,Γ12=cP(ˆLIn)UT+R,Γ16=P(IN1B)+IN1CTΔT,ˉΓ22=2R+U+UT,Δ=diag{δ1,δ2,,δm}.

    Remark 8. When σi0+, it can be concluded that (3.14) can not be satisfied, the data of each sampling time will be transmitted to the neighbor agents, that is, the event-triggered scheme reduces to the time-triggered scheme, thus bipartite leaderless consensus can be derived based on the sampled control law in [25].

    In this section, the bipartite leader-following consensus for Lur'e network is considered under the event-triggered communication scheme.

    Suppose that there is only one virtual leader, and the dynamics can be described by

    ˙x0(t)=Ax0(t)+Bf(Cx0(t)), (3.32)

    where x0(t)Rn is the position state of the virtual leader, the other notations and the dynamics of the followers are the same as those in (2.1).

    Definition 3. [25] Under Assumption 1, the signed Lur'e network (2.1) is said to achieve bipartite leader-following consensus if limtxi(t)wix0(t)=0 holds for all i=1,2,,N, where wi=1(iV1) and wi=1(iV2).

    Remark 9. In Definition 3, if iV1, we have wi=1, then limtxi(t)wix0(t)=0 reduces to limtxi(t)x0(t)=0. If iv2, we have wi=1, then limtxi(t)wix0(t)=0 reduces to limtxi(t)+x0(t)=0, which means xi(t)x0(t)(t), in other words, one part are synchronized to the state of the leader, the other part is synchronized to the opposite state of the leader, the structural balanced is critical to achieve the bipartite leader-following consensus.

    The following sampled control law is proposed to solve the bipartite leader-following consensus problem for Lur'e network (2.1) and (3.32) as follows

    ui(t)=cjN(i)|aij|[xi(tkh)sgn(aij)xj(tkh)]cdi[xi(tkh)wix0(tkh)](i=1,2,,N;t[tkh,tk+1h)), (3.33)

    where c>0 is the coupling strength, tkh(k=0,1,2,) is the transmitted instants, t0h=0, {t0,t1,}{0,1,2,}. di(i=1,2,,N) is the pining feedback gain, di>0 if agent i is pinned, otherwise di=0.

    Applying (3.33) to system (2.1), one has

    ˙xi(t)=Axi(t)+Bf(Cxi(t))cNj=1lijxj(tkh)cdi[xi(tkh)wix0(tkh)]. (3.34)

    Let ˉxi(t)=wixi(t) and ei(t)=ˉxi(t)x0(t)(i=1,2,,N), it follows from (3.34) that

    ˙ei(t)=Aei(t)+Bf(Cˉxi(t))Bf(Cx0(t))cNj=1ˉlijej(tkh)cdiei(tkh). (3.35)

    The event-triggered communication scheme is proposed as follows

    tk+1h=tkh+min{lh|[ei(tkh+lh)ei(tkh)]TΦ(1)i[ei(tkh+lh)ei(tkh)]>σieTi(tkh+lh)Φ(2)iei(tkh+lh)}, (3.36)

    where σi>0 are event trigger parameters, Φ(1)i and Φ(2)i(i=1,2,,N) are positive definite matrices to be designed.

    The interval [tkh,tk+1h) can be expressed as follows

    [tkh,tk+1h)=tk+11l=0[tkh+lh,tkh+lh+h). (3.37)

    Define τ(t)=t(tkh+lh), for t[tkh+lh,tkh+lh+h), one has 0τ(t)<h, let ˉei(tkh+lh)=ei(tkh+lh)ei(tkh)(i=2,3,,N), it can be derived that

    ei(tkh)=ei(tτ(t))ˉei(tkh+lh), (3.38)

    then for t[tkh,tk+1h), the event-triggered scheme can be rewritten as follows

    ˉeT(tkh+lh)Φ(1)ˉe(tkh+lh)eT(tτ(t))(σIn)Φ(2)e(tτ(t)), (3.39)

    where

    Φ(1)=diag{Φ(1)1,Φ(1)2,,Φ(1)N},Φ(2)=diag{Φ(2)1,Φ(2)2,,Φ(2)N},σ=diag{σ1,σ2,,σN}.

    From the above analysis, it can be known from (3.39) that the current sampled data ei(tkh+lh) will not be sent, which will be employed in the following consensus analysis.

    For convenience, some notations are given as

    e(t)=[eT1(t),eT2(t),,eTN(t)]T,η(t)=[ηT1(t),ηT2(t),,ηTN(t)]T,ηi(t)=[ηi1(t),ηi2(t),,ηim(t)]T,ηik(t)=fk(CTkˉxi(t))fk(CTkx0(t))(k=1,2,,m).

    Rewrite (3.35) in compact matrix form as

    ˙e(t)=(INA)e(t)c(ˆLIn)e(tτ(t))+c(ˆLIn)ˉe(tkh+lh)+(IN1B)η(t), (3.40)

    where ˆL=(ˆlij)N×N, ˆlij=ˉlij(ij),ˆlii=ˉlii+di.

    Theorem 2. Suppose that Assumption 1 holds, for given sampled period h>0, triggered parameters σi>0(i=1,2,,N), pinning feedback gain di and coupled strength c>0, the bipartite leader-following consensus can be reached in system (2.1)under the control law (39) and event-triggered scheme (3.39), if there exist some Nn×Nndefinite matrices P>0, Q>0, R>0, Ω>0 and T=diag{τ1,τ2,,τm}>0, and matrix URNn×Nn, such that the following matrix inequalities hold

    [Π11Π12UTcP(ˆLIn)π24ΩΠ16h(INA)TRh(INA)TΩΠ22UT+R000hc(ˆLIn)TRhc(ˆLIn)TΩQR00000Φ(1)00hc(ˆLIn)TRhc(ˆLIn)TΩπ24Ω000IN1Th(INB)TRh(INB)TΩR0Ω]<0, (3.41)
    [RUTR]>0, (3.42)

    where

    Π11=P(INA)+(INA)TP+QRπ24Ω,Π12=cP(ˆLIn)UT+R,Π16=P(INB)+INCTΔT,Π22=2R+U+UT+(σIn)Φ(2),Δ=diag{δ1,δ2,,δm}.

    Proof. Construct the following Lyapunov function candidate

    V(t)=4i=1Vi(t), (3.43)

    where

    V1(t)=eT(t)Pe(t),V2(t)=ttheT(s)Qe(s)ds,V3(t)=htthts˙eT(v)R˙e(v)dvds,V4(t)=h2ttkh+lh˙eT(s)Ω˙e(s)dsπ24ttkh+lh[e(s)e(tkh+lh)]TΩ[e(s)e(tkh+lh)]ds,

    where P,Q,R,ΩRNn×Nn>0.

    The proof method is similar to Theorem 1, the detailed process is omitted to save space.

    Notice that if σi=0(i=1,2,,N), the event-triggered scheme will reduce to the time-triggered scheme. In Theorem 2, let σi0+, we will obtain bipartite leader-following consensus under the sampled-data control.

    Corollary 2. Suppose that Assumption 1 holds, for given sampled period h>0, pinning feedback gain di and coupled strength c>0, the bipartite leader-following consensus under the sampled-data control can be reached in system (2.1) under the control law (3.33), if there exist some Nn×Nndefinite matrices P>0, Q>0, R>0, Ω>0 and T=diag{τ1,τ2,,τm}>0, and matrix URNn×Nn, such that the following matrix inequalities hold

    [Π11Π12UTπ24ΩΠ16h(INA)TRh(INA)TΩˉΠ22UT+R00hc(ˆLIn)TRhc(ˆLIn)TΩQR0000π24Ω000IN1Th(INB)TRh(INB)TΩR0Ω]<0, (3.44)
    [RUTR]>0, (3.45)

    where

    Π11=P(INA)+(INA)TP+QRπ24Ω,Π12=cP(ˆLIn)UT+R,Π16=P(INB)+INCTΔT,ˉΠ22=2R+U+UT,Δ=diag{δ1,δ2,,δm}.

    Remark 10. Similar to Remark 6 and Remark 7, the constrained optimization problem can be employed to find the maximum allowable sampling period h and the maximum values of the trigger parameters σi(i=1,2,,N).

    Remark 11. For structurally balanced topology, by gauge transformation, the leader-following bipartite consensus of signed network will be converted to the leader-following consensus of unsigned network, then the selected pinning nodes and controller gains can be designed according to the scheme in [68].

    This section provides two simulation examples to demonstrate the effectiveness of the proposed event-triggered communication scheme. The first example is for the bipartite leaderless consensus issue, and the second example is for the leader-following consensus issue.

    Consider a Lur'e network consisting of seven agents

    ˙xi(t)=Axi(t)+Bf(Cxi(t))+ui(t),(i=1,2,,7), (4.1)

    where

    xi(t)=[xi1(t)xi2(t)xi3(t)],A=[0.55981.3018011100.01350.0297],B=[0.884100],CT=[100],f(Cxi(t))=12(|xi1(t)+1||xi1(t)1|),

    it is easy to verify that the nonlinear function f() is an odd function with δk=1, which satisfies Assumption 1. The directed communication topology is shown in Figure 1, the solid lines denote the cooperative interactions, the dashed lines denote the antagonistic interactions, obviously, which can be divided into two sub-networks V1={1,2,3,4} and V2={5,6,7}, the agents are cooperative in V1 or V2, the agents are competitive between V1 and V2, by using Lemma 1, we can obtain the gauge transformation W=diag{1,1,1,1,1,1,1}.

    Figure 1.  Illustration for the communication topology.

    Case 1: Bipartite leaderless consensus

    Let the coupling strength c=10, the sampling period h=0.01, the event-triggered parameters are given as σi=0.1(i=2,3,,7). By using Theorem 1, the related matrices can be obtained, which means bipartite leaderless consensus can be achieved, the event-triggered matrices are given as follows

    Φ(1)2=[72.96356.88291.41106.882960.43711.31221.41101.312261.1014]Φ(2)2=[6.12014.59020.88254.590228.76512.03520.88252.035227.5160],Φ(1)3=[76.84738.06961.56188.069658.87431.34431.56181.344359.4599]Φ(2)3=[1.47532.37640.79662.376415.03252.96940.79662.969412.3513],Φ(1)4=[72.95177.12221.48627.122257.82931.13901.48621.139058.2693]Φ(2)4=[2.29503.27440.95813.274419.88163.19540.95813.195417.2817],Φ(1)5=[89.50638.09611.53718.096166.90951.36601.53711.366068.3950]Φ(2)5=[5.05613.16290.81593.162924.54822.17270.81592.172722.4386],Φ(1)6=[82.25578.11101.59338.111060.88311.40771.59331.407761.9600]Φ(2)6=[2.00902.51100.77392.511016.71582.69630.77392.696314.1200],Φ(1)7=[73.66017.59711.49957.597157.96471.22071.49951.220758.3560]Φ(2)7=[1.54712.60180.86652.601816.02623.19210.86653.192113.2993],

    taking t[0,2), the simulation results show that only 54 sampled data is used, which takes 27% of the whole sampled signals. Moreover, it can be computed that the average sampling period is 0.0369, which is 3.69 times of the sampling period h=0.01. The release instants and release intervals are illustrated in Figure 2. The curve of xij(t)(i=1,2,,7;j=1,2,3) are presented in Figure 3, it can be seen the bipartite leaderless consensus is achieved. In order to show the benefits of our proposed event-triggered scheme than [39,40,45,46], some comparisons are given in Table 1, it can be seen that our event-triggered scheme can lead to larger sampling period h, that is, our results are less conservative than [39,40,45,46]. In addition, by using Remark 7, the upper bound of σi is shown in Table 2 for given coupling strength c=10 and sampled period h=0.01, which plays an important role owing to a big value can further save the network resources.

    Figure 2.  The release instants and release intervals under the event-triggered scheme.
    Figure 3.  The trajectories of the state xij(t)(i=1,2,,7;j=1,2,3) for bipartite leaderless consensus of signed Lur'e network.
    Table 1.  The upper bound of sampling period h for bipartite leaderless consensus.
    Trigger scheme Trigger matrix Coupling strength Trigger parameter Upper bound of h
    Our scheme Φ(1)iΦ(2)i c=10 σi=0.01 0.0227
    [41,42,43,44] Φ(1)i=Φ(2)i c=10 σi=0.01 0.0089

     | Show Table
    DownLoad: CSV
    Table 2.  The upper bound of triggered parameter σi.
    Type Trigger matrix Coupling strength Sampled period Upper bound of σi
    Leaderless Φ(1)i=Φ(2)i c=10 h=0.01 0.0086
    Leader-following Φ(1)i=Φ(2)i c=10 h=0.01 0.4681

     | Show Table
    DownLoad: CSV

    If the sampling period takes it's upper bound, that is h=0.0227, taking t[0,2), the simulation results show that only 53 sampled data is used. The release instants and release intervals are illustrated in Figure 4. The curve of xij(t)(i=1,2,,7;j=1,2,3) are presented in Figure 5, it can be seen the bipartite leaderless consensus is achieved, the event-triggered matrices are given as follows

    Φ(1)2=[11.54434.13831.86454.13834.42360.94581.86450.94583.5124]Φ(2)2=[0.00010.00030.00360.00030.00110.01740.00360.01741.0540],Φ(1)3=[13.00834.45851.94854.45854.33010.88271.94850.88273.5738]Φ(2)3=[00.00010.00180.00010.00040.00760.00180.00760.3599],Φ(1)4=[12.19864.36121.94344.36124.40440.96091.94340.96093.4542]Φ(2)4=[00.00010.00220.00010.00050.01000.00220.01000.5528],Φ(1)5=[14.76714.71522.02674.71524.66740.76782.02670.76784.3585]Φ(2)5=[0.01410.04290.02690.04290.13110.08590.02690.08590.7288],
    Φ(1)6=[14.05974.69112.02404.69114.41590.85502.02400.85503.8012]Φ(2)6=[0.00010.00030.00270.00030.00100.01080.00270.01080.4249],Φ(1)7=[12.43794.35381.92474.35384.34540.91951.92470.91953.4819]Φ(2)7=[00.00010.00170.00010.00040.00760.00170.00760.3942].
    Figure 4.  The release instants and release intervals under the event-triggered scheme.
    Figure 5.  The trajectories of the state xij(t)(i=1,2,,7;j=1,2,3) for bipartite leaderless consensus of signed Lur'e network.

    Case 2: Bipartite leader-following consensus

    Suppose that the dynamics of nonlinear multi-agent systems is the same as Case 1, and the communication topology is shown in Figure 6, which is structurally balanced. Let V1={1,2,3} and V2={4,5,6}, then the agents are cooperative in V1 or V2, the agents are competitive between V1 and V2, then we can obtain W=diag{1,1,1,1,1,1}. Take c=10, the sampling period h=0.01, the event-triggered parameters σi=0.1(i=1,2,,6), the pinning feedback gain D=diag{2,0,0,0,2,0}, By using Theorem 2, the event-triggered matrices can be obtained as follows

    Φ(1)1=[3.15110.03930.00190.03932.94650.02690.00190.02693.0081]Φ(2)1=[1.79090.00060.00080.00061.86010.01010.00080.01011.8412],Φ(1)2=[3.06660.05750.00400.05752.90730.03660.00400.03662.9949]Φ(2)2=[1.61820.00920.00340.00921.79340.02230.00340.02231.7562],Φ(1)3=[2.57290.03880.00550.03882.60220.00110.00550.00112.6193]Φ(2)3=[1.49080.01640.00510.01641.72760.02950.00510.02951.6795],Φ(1)4=[3.06660.05750.00400.05752.90730.03660.00400.03662.9949]Φ(2)4=[1.61820.00920.00340.00921.79340.02230.00340.02231.7562],Φ(1)5=[3.15110.03930.00190.03932.94650.02690.00190.02693.0081]Φ(2)5=[1.79090.00060.00080.00061.86010.01010.00080.01011.8412],Φ(1)6=[2.57290.03880.00550.03882.60220.00110.00550.00112.6193]Φ(2)6=[1.49080.01640.00510.01641.72760.02950.00510.02951.6795],
    Figure 6.  Illustration for the communication topology.

    taking t[0,1), the simulation results show that only 33 sampled data is sent out, which takes 33% of the sampled signals. Moreover, it can be computed that our event-triggered scheme can obtain an average sampling period of 0.03, the release instants and release intervals are illustrated in Figure 7. The curve of xij(t)(i=1,2,,6;j=1,2,3) are presented in Figure 8, which can be seen the bipartite leader-following consensus is achieved. In order to show the benefits of our proposed triggered mechanism than [39,40,45,46], some comparisons are given in Table 3, it can be seen that our event-triggered scheme can lead to larger sampling period h, that is, our results are less conservative than [39,40,45,46]. In addition, by using Theorem 2 and the similar method of Remark 7, the upper bound of σi is shown in Table 2 given the coupling strength c=10 and the sampled period h=0.01.

    Figure 7.  The release instants and release intervals under the event-triggered scheme.
    Figure 8.  The trajectories of the state xij(t)(i=1,2,,6;j=1,2,3) for bipartite leader-following consensus of signed Lur'e network.
    Table 3.  The upper bound of sampling period h for bipartite leader-following consensus.
    Trigger scheme Trigger matrix Coupling strength Trigger parameter Upper bound of h
    Our scheme Φ(1)iΦ(2)i c=10 σi=0.01 0.0612
    [41,42,43,44] Φ(1)i=Φ(2)i c=10 σi=0.01 0.0552

     | Show Table
    DownLoad: CSV

    The event-triggered communication scheme has been proposed to study the bipartite consensus of Lur'e system in the signed networks. By utilizing matrix transformation techniques and algebraic graph theories, some sufficient conditions in terms of LMIs have been established to ensure that both bipartite leaderless and leader-following consensus can be achieved. Compared with some existing event-triggered results, the simulation examples have shown that the proposed event-triggered communication scheme has the advantage to achieve a better performance. It should be emphasized that the sub-network topology is not required to be connected. Notice that the above results are based on the event-triggered parameters that are given as positive constants, it would be interesting to further investigate adaptive event-triggered communication scheme for bipartite consensus, where the triggered parameters can be adjusted with respect to the dynamic errors. The related results can also be extended to the cases with switching topology and time-delay and so on.

    This work was jointly supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY17F030020. Jiaxing science and technology project under Grant No. 2016AY13011 and 2016AY13013.

    The author declare no conflicts of interest in this paper.



    [1] A. Sharma, D. Srinivasan, A. Trivedi, A decentralized multiagent system approach for service restoration using DG islanding, IEEE T. Smart Grid, 6 (2015), 2784–2793. http://dx.doi.org/10.1109/TSG.2015.2418334 doi: 10.1109/TSG.2015.2418334
    [2] B. Jiang, M. Deghat, B. D. O. Anderson, Simultaneous velocity and position estimation via distance-only measurements with application to multi-agent system control, IEEE T. Automat. Contr., 62 (2016), 869–875. http://dx.doi.org/10.1109/TAC.2016.2558040 doi: 10.1109/TAC.2016.2558040
    [3] H. Li, J. Zhang, L. Jing, W. Ying, Neural-network-based adaptive quasi-consensus of nonlinear multi-agent systems with communication constrains and switching topologies, Nonlinear Anal. Hybri., 35 (2020), 100833. https://doi.org/10.1016/j.nahs.2019.100833 doi: 10.1016/j.nahs.2019.100833
    [4] H. Shen, Y. Wang, J. Xia, J. H. Park, Z. Wang, Fault-tolerant leader-following consensus for multi-agent systems subject to semi-Markov switching topologies: An event-triggered control scheme, Nonlinear Anal. Hybri., 34 (2019), 92–107. https://doi.org/10.1016/j.nahs.2019.05.003 doi: 10.1016/j.nahs.2019.05.003
    [5] R. Olfati-Saber, R. M. Murray, Consensus problems in networks of agents with switching topology and time-delays, IEEE T. Automat. Contr., 49 (2004), 1520–1533. https://doi.org/10.1109/TAC.2004.834113 doi: 10.1109/TAC.2004.834113
    [6] W. Ren, R. W. Beard, Consensus seeking in multiagent systems under dynamically changing interaction topologies, IEEE T. Automat. Contr., 50 (2005), 655–661. https://doi.org/10.1109/TAC.2005.846556 doi: 10.1109/TAC.2005.846556
    [7] W. Yu, G. Chen, M. Cao, Some necessary and suncient conditions for second-order consensus in multi-agent dynamical systems, Automatica, 46 (2010), 1089–1095. https://doi.org/10.1016/j.automatica.2010.03.006 doi: 10.1016/j.automatica.2010.03.006
    [8] Z. Liu, X. Hu, M. Ge, Y. Wang, Asynchronous impulsive control for consensus of second-order multi-agent networks, Commun. Nonlinear Sci. Numer. Simul., 79 (2019), 104892. https://doi.org/10.1016/j.cnsns.2019.104892 doi: 10.1016/j.cnsns.2019.104892
    [9] H. Yu, X. Xia, Adaptive leaderless consensus of agents in jointly connected networks, Neurocomputing, 241 (2017), 64–70. https://doi.org/10.1016/j.neucom.2017.02.031 doi: 10.1016/j.neucom.2017.02.031
    [10] H. Li, Y. Zhu, L. Jing, W. Ying, Consensus of second-order delayed nonlinear multi-agent systems via node-based distributed adaptive completely intermittent protocols, Appl. Math. Comput., 326 (2018), 1–15. https://doi.org/10.1016/j.amc.2018.01.005 doi: 10.1016/j.amc.2018.01.005
    [11] Y. Hong, G. Chen, L. Bushnell, Distributed observers design for leader-following control of multi-agent networks, Automatica, 44 (2008), 846–850. https://doi.org/10.1016/j.Automatica.2007.07.004 doi: 10.1016/j.Automatica.2007.07.004
    [12] Y. Wang, H. Li, X. Qiu, X. Xie, Consensus tracking for nonlinear multi-agent systems with unknown disturbance by using model free adaptive iterative learning control, Appl. Math. Comput., 365 (2020), 124701. https://doi.org/10.1016/j.amc.2019.124701 doi: 10.1016/j.amc.2019.124701
    [13] L. Shi, Y. Xiao, J. Shao, W. Zheng, Containment control of asynchronous discrete-time general linear multiagent systems with arbitrary network topology, IEEE T. Cybernetics, 50 (2020), 2546–2556. https://doi.org/10.1109/TCYB.2019.2915941 doi: 10.1109/TCYB.2019.2915941
    [14] H. Li, Leader-following consensus of nonlinear multi-agent systems with mixed delays and uncertain parameters via adaptive pinning intermittent control, Nonlinear Anal. Hybri., 22 (2016), 202–214. https://doi.org/10.1016/j.nahs.2016.04.004 doi: 10.1016/j.nahs.2016.04.004
    [15] M. Lu, J. Wu, X. Zhan, T. Han, H. Yan, Consensus of second-order heterogeneous multi-agent systems with and without input saturation, ISA T., 2021. https://doi.org/10.1016/j.isatra.2021.08.001
    [16] D. Easley, J. Kleinberg, Networks, crowds, and markets: Reasoning about a highly connected world, Significance, 9 (2012), 43–44.
    [17] S. Wasserman, K. Faust, Social network analysis: Methods and applications, Cambridge university press, 1994.
    [18] J. Shao, L. Shi, Y. Cheng, T. Li, Asynchronous tracking control of leader-follower multiagent systems with input uncertainties over switching signed digraphs, IEEE T. Cybernetics, 2021. https://doi.org/10.1109/TCYB.2020.3044627
    [19] J. Shao, W. Zheng, L. Shi, Y. Cheng, Bipartite tracking consensus of generic linear agents with discrete-time dynamics over cooperation-competition networks, IEEE T. Cybernetics, 51 (2021), 5225–5235. https://doi.org/10.1109/TCYB.2019.2957415 doi: 10.1109/TCYB.2019.2957415
    [20] L. Shi, Y. Cheng, J. Shao, X. Zhang, Collective behavior of multileader multiagent systems with random interactions over signed digraphs, IEEE T. Control. Netw., 8 (2021), 1394–1405. https://doi.org/10.1109/TCNS.2021.3065650 doi: 10.1109/TCNS.2021.3065650
    [21] X. Zhan, L. Hao, T. Han, H. Yan, Adaptive bipartite output consensus for heterogeneous multi-agent systems with quantized information: A fixed-time approach, J. Franklin I., 358 (2021), 7221–7236. https://doi.org/10.1016/j.jfranklin.2021.07.009 doi: 10.1016/j.jfranklin.2021.07.009
    [22] J. Wu, Q. Deng, T. Han, H. Yan, Bipartite output regulation for singular heterogeneous multi-agent systems on signed graph, Asian J. Control, 2021. https://doi.org/10.1002/asjc.2654
    [23] C. Altafini, Consensus problems on networks with antagonistic interactions, IEEE T. Automat. Contr., 58 (2013), 935–946. https://doi.org/10.1109/TAC.2012.2224251 doi: 10.1109/TAC.2012.2224251
    [24] D. Meng, M. Du, Y. Jia, Interval bipartite consensus of networked agents associated with signed digraphs, IEEE T. Automat. Contr., 61 (2016), 3755–3770. https://doi.org/10.1109/TAC.2016.2528539 doi: 10.1109/TAC.2016.2528539
    [25] F. Liu, Q. Song, G. Wen, J. Lu, J. Cao, Bipartite synchronization of Lur'e network under signed digraph, Int. J. Robust Nonlin., 28 (2018), 6087–6105. https://doi.org/10.1002/rnc.4358 doi: 10.1002/rnc.4358
    [26] F. Liu, Q. Song, G. Wen, J. Cao, X. Yang, Bipartite synchronization in coupled delayed neural networks under pinning control, Neural Networks, 108 (2018), 146–154. https://doi.org/10.1016/j.neunet.2018.08.009 doi: 10.1016/j.neunet.2018.08.009
    [27] J. Ren, Q. Song, G. Lu, Event-triggered bipartite leader-following consensus of second-order nonlinear multi-agent systems under signed digraph, J. Franklin I., 356 (2019), 6591–6609. https://doi.org/10.1016/j.jfranklin.2019.06.034 doi: 10.1016/j.jfranklin.2019.06.034
    [28] J. Ren, Q. Song, Y. Gao, G. Lu, Leader-following bipartite consensus of second-order time-delay nonlinear multi-agent systems with event-triggered pinning control under signed digraph, Neurocomputing, 385 (2020), 186–196. https://doi.org/10.1016/j.neucom.2019.12.043 doi: 10.1016/j.neucom.2019.12.043
    [29] H. Zhang, J. Chen, Bipartite consensus of multi-agent systems over signed graphs: State feedback and output feedback control approaches, Int. J. Robust Nonlin., 27 (2017), 3–14. https://doi.org/10.1002/rnc.3552 doi: 10.1002/rnc.3552
    [30] G. Wen, H. Wang, X. Yu, W. Yu, Bipartite tracking consensus of linear multi-agent systems with a dynamic leader, IEEE T. Circuits-II, 65 (2018), 1204–1208. https://doi.org/10.1109/TCSII.2017.2777458 doi: 10.1109/TCSII.2017.2777458
    [31] H. Wang, W. Yu, G. Wen, G. Chen, Finite-time bipartite consensus for multi-agent systems on directed signed networks, IEEE T. Circuits-I, 65 (2018), 4336–4348. https://doi.org/10.1109/TCSI.2018.2838087 doi: 10.1109/TCSI.2018.2838087
    [32] J. P. Hu, Y. Wu, L. Liu, G. Feng, Adaptive bipartite consensus control of high-order multiagent systems on coopetition networks, Int. J. Robust Nonlin., 28 (2018), 2868–2886. https://doi.org/10.1002/rnc.4054 doi: 10.1002/rnc.4054
    [33] H. Hu, W. Yu, G. Wen, Q. Xuan, J. Cao, Reverse group consensus of multi-agent systems in the cooperation-competition network, IEEE T. Circuits-I, 63 (2016), 2036–2047. https://doi.org/10.1109/TCSI.2016.2591264 doi: 10.1109/TCSI.2016.2591264
    [34] X. Ai, Adaptive robust bipartite consensus of high-order uncertain multi-agent systems over cooperation-competition networks, J. Franklin I., 357 (2020), 1813–1831. https://doi.org/10.1016/j.jfranklin.2019.12.038 doi: 10.1016/j.jfranklin.2019.12.038
    [35] D. V. Dimarogonas, E. Frazzoli, K. H. Johansson, Distributed event-triggered control for multi-agent systems, IEEE T. Automat. Contr., 57 (2011), 1291–1297. https://doi.org/10.1109/TAC.2011.2174666 doi: 10.1109/TAC.2011.2174666
    [36] P. Tabuada, Event-triggered real-time scheduling of stabilizing control tasks, IEEE T. Automat. Contr., 52 (2007), 1680–1685. https://doi.org/10.1109/TAC.2007.904277 doi: 10.1109/TAC.2007.904277
    [37] X. Wang, M. D. Lemmon, Self-triggered feedback control systems with finite-gain L2 stability, IEEE T. Automat. Contr., 54 (2009), 452–467. https://doi.org/10.1109/TAC.2009.2012973 doi: 10.1109/TAC.2009.2012973
    [38] M. Mazo, P. Tabuada, Decentralized event-triggered control over wireless sensor/actuator networks, IEEE T. Automat. Contr, 56 (2011), 2456–2461. https://doi.org/10.1109/TAC.2011.2164036 doi: 10.1109/TAC.2011.2164036
    [39] D. Yue, E. Tian, Q. Han, A delay system method for designing event-triggered controllers of networked control systems, IEEE T. Automat. Contr., 58 (2012), 475–481. https://doi.org/10.1109/TAC.2012.2206694 doi: 10.1109/TAC.2012.2206694
    [40] C. Peng, Q. Han, A novel event-triggered transmission scheme and L2 control co-design for sampled-data control systems, IEEE T. Automat. Contr., 58 (2013), 2620–2626. https://doi.org/10.1109/TAC.2013.2256015 doi: 10.1109/TAC.2013.2256015
    [41] X. Yin, D. Yue, S. Hu, Adaptive periodic event-triggered consensus for multi-agent systems subject to input saturation, Int. J. Control, 89 (2016), 653–667. https://doi.org/10.1080/00207179.2015.1088967 doi: 10.1080/00207179.2015.1088967
    [42] A. Hu, J. Cao, M. Hu, L. Guo, Event-triggered group consensus for multi-agent systems subject to input saturation, J. Franklin I., 355 (2018), 7384–7400. doi: https://doi.org/10.1016/j.jfranklin.2018.07.024 doi: 10.1016/j.jfranklin.2018.07.024
    [43] W. Zhu, Z. Jiang, G. Feng, Event-based consensus of multi-agent systems with general linear models, Automatica, 50 (2014), 552–558. https://doi.org/10.1016/j.automatica.2013.11.023 doi: 10.1016/j.automatica.2013.11.023
    [44] W. Xu, D. W. C. Ho, L. Li, J. Cao, Event-triggered schemes on leader-following consensus of general linear multiagent systems under different topologies, IEEE T. Cybernetics, 47 (2015), 212–223. https://doi.org/10.1109/TCYB.2015.2510746 doi: 10.1109/TCYB.2015.2510746
    [45] Z. Cheng, D. Yue, S. Hu, H. Ge, L. Chen, Distributed event-triggered consensus of multi-agent systems under periodic DoS jamming attacks, Neurocomputing, 400 (2020), 458–466. https://doi.org/10.1016/j.neucom.2019.03.089 doi: 10.1016/j.neucom.2019.03.089
    [46] l. Zha, J. Liu, J. Cao, Resilient event-triggered consensus control for nonlinear muti-agent systems with DoS attacks, J. Franklin I., 356 (2019), 7071–7090. https://doi.org/10.1016/j.jfranklin.2019.06.014 doi: 10.1016/j.jfranklin.2019.06.014
    [47] L. Xing, C. Wen, Z. Liu, H. Su, J. Cai, Event-triggered adaptive control for a class of uncertain nonlinear systems, IEEE T. Automat. Contr., 62 (2016), 2071–2076. https://doi.org/10.1109/TAC.2016.2594204 doi: 10.1109/TAC.2016.2594204
    [48] C. Peng, J. Zhang, Q. Han, Consensus of multiagent systems with nonlinear dynamics using an integrated sampled-data-based event-triggered communication scheme, IEEE Trans. Syst. Man Cybern. Syst., 49 (2018), 589–599. https://doi.org/10.1109/TSMC.2018.2814572 doi: 10.1109/TSMC.2018.2814572
    [49] H. Zhang, P. Hu, Z. Liu, L. Ding, Consensus analysis for multi-agent systems via periodic event-triggered algorithms with quantized information, J. Franklin I., 354 (2017), 6364–6380. https://doi.org/10.1016/j.jfranklin.2017.08.003 doi: 10.1016/j.jfranklin.2017.08.003
    [50] H. Zhang, D. Yue, X. Yin, S. Hu, C. Dou, Finite-time distributed event-triggered consensus control for multi-agent systems, Inform. Sciences, 339 (2016), 132–142. https://doi.org/10.1016/j.ins.2015.12.031 doi: 10.1016/j.ins.2015.12.031
    [51] H. Zhang, G. Feng, H. Yan, Q. Chen, Observer-based output feedback event-triggered control for consensus of multi-agent systems, IEEE T. Ind. Electron., 61 (2013), 4885–4894. https://doi.org/10.1109/TIE.2013.2290757 doi: 10.1109/TIE.2013.2290757
    [52] Y. Wu, Z. Wang, H. Zhang, C. Huang, Output-based event-triggered consensus of general linear multi-agent systems with communication delay under directed graphs, J. Franklin I., 357 (2020), 3702–3720. https://doi.org/10.1016/j.jfranklin.2020.02.036 doi: 10.1016/j.jfranklin.2020.02.036
    [53] A. Hu, Y. Wang, J. Cao, A. Alsaedi, Event-triggered bipartite consensus of multi-agent systems with switching partial couplings and topologies, Inform. Sciences, 521 (2020), 1–13. https://doi.org/10.1016/j.ins.2020.02.038 doi: 10.1016/j.ins.2020.02.038
    [54] W. He, S. Lv, X. Wang, F. Qian, Leaderless consensus of multi-agent systems via an event-triggered strategy under stochastic sampling, J. Franklin I., 356 (2019), 6502–6524. https://doi.org/10.1016/j.jfranklin.2019.05.033 doi: 10.1016/j.jfranklin.2019.05.033
    [55] Y. Cui, L. Xu, Bounded average consensus for multi-agent systems with switching topologies by event-triggered persistent dwell time control, J. Franklin I., 356 (2019), 9095–9121. https://doi.org/10.1016/j.jfranklin.2019.07.016 doi: 10.1016/j.jfranklin.2019.07.016
    [56] N. Mu, X. Liao, T. Huang, Consensus of second-order multi-agent systems with random sampling via event-triggered control, J. Franklin I., 353 (2016), 1423–1435. https://doi.org/10.1016/j.jfranklin.2016.01.014 doi: 10.1016/j.jfranklin.2016.01.014
    [57] Y. Yang, D. Yue, C. Xu, Dynamic event-triggered leader-following consensus control of a class of linear multi-agent systems, J. Franklin I., 355 (2018), 7706–7734. https://doi.org/10.1016/j.jfranklin.2018.08.007 doi: 10.1016/j.jfranklin.2018.08.007
    [58] W. He, B. Xu, Q. Han, F. Qian, Adaptive consensus control of linear multiagent systems with dynamic event-triggered strategies, IEEE T. Cybernetics, 50 (2020), 2996–3008. https://doi.org/10.1109/TCYB.2019.2920093 doi: 10.1109/TCYB.2019.2920093
    [59] X. Chen, H. Yu, F. Hao, Prescribed-time event-triggered bipartite consensus of multiagent systems, IEEE T. Cybernetics, 2020. https://doi.org/10.1109/TCYB.2020.3004572
    [60] Y. Cai, H. Zhang, J. Duan, J. Zhang, Distributed bipartite consensus of linear multiagent systems based on event-triggered output feedback control scheme, IEEE Trans. Syst. Man Cybern. Syst., 51 (2021), 6743–6756. https://doi.org/10.1109/TSMC.2020.2964394 doi: 10.1109/TSMC.2020.2964394
    [61] A. Sharifi, M. Pourgholi, Fixed-time bipartite consensus of nonlinear multi-agent systems using event-triggered control design, J. Franklin I., 358 (2021), 9178–9198. https://doi.org/10.1016/j.jfranklin.2021.09.023 doi: 10.1016/j.jfranklin.2021.09.023
    [62] Y. Xu, J. Wang, Y. Zhang, Y. Xu, Event-triggered bipartite consensus for high-order multi-agent systems with input saturation, Neurocomputing, 379 (2020), 284–295. https://doi.org/10.1016/j.neucom.2019.10.095 doi: 10.1016/j.neucom.2019.10.095
    [63] M. Zhao, C. Peng, W. He, Y. Song, Event-triggered communication for leader-following consensus of second-order multiagent systems, IEEE T. Cybernetics, 48 (2018), 1888–1897. https://doi.org/10.1109/TCYB.2017.2716970 doi: 10.1109/TCYB.2017.2716970
    [64] K. Gu, J. Chen, V. L. Kharitonov, Stability of time-delay systems, Springer Science and Business Media, 2003.
    [65] K. Gu, An integral inequality in the stability problem of time-delay systems, Proceedings of the 39th IEEE Conference on Decision and Control, 3 (2000), 2805–2810. https://doi.org/10.1109/CDC.2000.914233 doi: 10.1109/CDC.2000.914233
    [66] E. Fridman, Introduction to time-delay systems: Analysis and control, Springer, 2014.
    [67] S. Boyd, L. E. Chaoui, E. Feron, V. Balakrishnan, Linear matrix inequalities in system and control theory, Society for Industrial and Applied Mathematics, 1994.
    [68] Q. Song, J. Cao, W. Yu, Second-order leader-following consensus of nonlinear multi-agent systems via pinning control, Syst. Control. Lett., 59 (2010), 553–562. https://doi.org/10.1016/j.sysconle.2010.06.016 doi: 10.1016/j.sysconle.2010.06.016
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