Research article

Dynamical analysis and boundedness for a generalized chaotic Lorenz model

  • Received: 06 April 2023 Revised: 23 May 2023 Accepted: 05 June 2023 Published: 12 June 2023
  • MSC : 34H10, 34H15, 34H20

  • The dynamical behavior of a 5-dimensional Lorenz model (5DLM) is investigated. Bifurcation diagrams address the chaotic and periodic behaviors associated with the bifurcation parameter. The Hamilton energy and its dependence on the stability of the dynamical system are presented. The global exponential attractive set (GEAS) is estimated in different 3-dimensional projection planes. A more conservative bound for the system is determined, that can be applied in synchronization and chaos control of dynamical systems. Finally, the finite time synchronization of the 5DLM, indicating the role of the ultimate bound for each variable, is studied. Simulations illustrate the effectiveness of the achieved theoretical results.

    Citation: Xinna Mao, Hongwei Feng, Maryam A. Al-Towailb, Hassan Saberi-Nik. Dynamical analysis and boundedness for a generalized chaotic Lorenz model[J]. AIMS Mathematics, 2023, 8(8): 19719-19742. doi: 10.3934/math.20231005

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  • The dynamical behavior of a 5-dimensional Lorenz model (5DLM) is investigated. Bifurcation diagrams address the chaotic and periodic behaviors associated with the bifurcation parameter. The Hamilton energy and its dependence on the stability of the dynamical system are presented. The global exponential attractive set (GEAS) is estimated in different 3-dimensional projection planes. A more conservative bound for the system is determined, that can be applied in synchronization and chaos control of dynamical systems. Finally, the finite time synchronization of the 5DLM, indicating the role of the ultimate bound for each variable, is studied. Simulations illustrate the effectiveness of the achieved theoretical results.



    Chaos is very interesting nonlinear phenomenon and has applications in many areas such as biology, economics, signal generator design, secure communication, many other engineering systems and so on. Because a nonlinear system in the chaotic state is very sensitive to its initial condition and chaos causes often irregular behavior in practical systems, chaos is sometimes undesirable [1,2,3,4,5,6,7]. The 3-dimensional Lorenz model (3DLM) was the first chaotic system proposed in the literature [3]. Celikovsky and Chen [2,8,9] introduced generalized Lorenz canonical forms, covering a large class of 3-dimensional autonomous systems. Further, Zhang et al. [10] by combining the advantages of both integer-order and fractional-order complex chaotic systems, proposed a hybrid-order complex Lorenz system. Chaotic systems have been widely addressed both from their mathematical properties [11,12] and practical applications [11,12,13,14,15].

    The 3DLM reveals the dependence of the solutions on the initial conditions for chaotic situations. Higher order Lorenz models have been derived to further study the stability of solutions and paths to chaos. The 3DLM, was obtained from the Rayleigh-Benard convection equations, which combine dissipative, heating and nonlinear advection physical processes. An interesting topic is the investigation of modes in the 3DLM and its generalization to higher dimensional models by increasing the number of modes. Shen [16,17] generalized the 3DLM to the 5DLM by adding two additional Fourier modes. This led to a better understanding of the role of some variables in the stability of solutions. Therefore, the 5DLM allows the role of modes in the predictability of solutions to be investigated for further understanding of the variables that increase the stability of solutions, and to find analytical solutions for critical points. Although the role of the modes on the stability of solutions, as well as some dynamical properties of the 5DLM, have been studied, the calculation of bounds for the variables and the global dynamics of the 5DLM are still open and challenging problems. Shen also extended the 5DLM to a six-dimensional Lorenz model (6DLM) [18], seven-dimensional Lorenz model (7DLM) [19] and generalized Lorenz model (GLM) [20], in order to examine the impact of an additional mode and its accompanying heating term on solution stability.

    Given a chaotic dynamical system, if its chaotic attractor is bounded in the phase space and the trajectories of the system remain in a bounded region of the phase space, then we say that "the chaotic dynamical system is bounded". Estimation of the ultimate bound set (UBS) and the positive invariant set (PIS) of a chaotic system plays a very important role in studying its dynamic behavior. Among the most important applications, one can mention their use in controlling and synchronizing chaotic systems [21,22,23,24,25,26,27,28,29]. In fact, the bounds are necessary for both theoretical studies of chaotic attractors and numerical search of attractors. If we can show that, under certain considerations, there exists a GEAS for a chaotic system, then we can conclude that the system cannot have periodic or quasi-periodic responses, equilibrium points, or hidden attractors, outside this set of attractors. This issue has a great application in controlling systems and preventing their possible problems. Leonov [21] derived the first results about global UBS for the Lorenz model. Subsequently, Swinnerton-Dyer [30] demonstrated that the bounds of the states of the Lorenz equations could be determined by using Lyapunov functions. Several researchers further developed the idea and computed the GEAS and PIS for different chaotic systems [31,32,33,34].

    To the best of our knowledge, the GEAS and UBS for the 5DLM have not been investigated yet. In the present work, by changing system parameters and conditions, we create different attractive sets. Also, we calculate a small attractive set only dependent on the system parameters. The results obtained from the UBS have been used in the synchronization and control of dynamical systems [35,36,37]. Due to the importance of minimizing the synchronization time, by applying a finite time control scheme, an efficient synchronization method is given based on the obtained ultimate bound [38,39]. By developing these techniques, we can also estimate the ultimate bound of fractional chaotic systems [40,41,42].

    This article is organized into 6 sections. The dynamical behavior of the 5DLM, including phase portraits, bifurcation diagrams and Hamilton energy are given in Section 2. In Section 3, we introduce some preliminary definitions and GEAS of the system. In Section 4, we present a method to compute small bound for the 5DLM. Section 5 presents the finite time synchronization problem using the results obtained in Section 4. The main conclusions are given in Section 6.

    The Rayleigh-Benard model for 2-dimensional (x,z), dissipative and forced convection is [3]:

    t2ψ=(ψ,2ψ)(x,z)+ν4ψ+gγθx, (2.1)
    tθ=(ψ,θ)(x,z)+ΔTHψx+κ2θ. (2.2)

    According to the studies of Rayleigh [3] and Saltzman [4], the following equations were obtained

    a(1+a2)1κ1ψ=x12sin(πaH1x)sin(πH1z), (2.3)
    πR1cRaΔT1θ=x22cos(πaH1x)sin(πH1z)x3sin(2πH1z), (2.4)

    where, x1,x2 and x3 are function of time alone. All the parameters and variables mentioned above are given in Table 1.

    Table 1.  The parameters and variables in Eqs (2.1)–(2.5).
    Parameter Meaning
    ψ stream function
    θ temperature perturbation
    g gravity acceleration
    γ thermal expansion coefficient
    ν kinematic viscosity
    κ thermal diffusivity
    ΔT temperature difference
    Ra Rayleigh number
    Rc free-slip critical Rayleigh value
    a ratio of vertical and horizontal scale

     | Show Table
    DownLoad: CSV

    By making some changes and manipulations, the partial differential equations (2.1) and (2.2) are converted into ordinary differential equations. Thus, the 3DLM chaotic system is expressed as [3]:

    ˙x1=σ(x2x1),˙x2=x1x3+rx1x2,˙x3=x1x2bx3, (2.5)

    where, σ,r are the Prandtl number, normalized Rayleigh number or the heating parameter and b=41+a2. Shen et al. [16] extended the 3DLM to the five-dimensional LM (5DLM) by including two additional Fourier modes with two additional vertical wave numbers. They used the five Fourier modes and rewrote ψ and θ as the following:

    ψ=κ1+a2a(x1M1), (2.6)
    θ=ΔTπRcRa(x2M2x3M3+x4M5x5M6), (2.7)

    where,

    M1=2sin(lx)sin(mz),M2=2cos(lx)sin(mz),M3=sin(2mz),M5=2cos(lx)sin(3mz),M6=sin(4mz).

    An additional mode M4=2sin(lx)sin(3mz) is included to derive the 6DLM. Here, l and m are defined as πaH and πH, representing the horizontal and vertical wave numbers, respectively, and H is the domain height and 2Ha represents the domain width.

    By coordinate transformation, the original equation can be reduced to the following five-dimensional nonlinear dynamics:

    ˙x1=σ(x2x1),˙x2=x1x3+rx1x2,˙x3=x1x2x1x4bx3,˙x4=x1x32x1x5dx4,˙x5=2x1x44bx5, (2.8)

    where d=9+a21+a2.

    Numerical analysis shows that the dynamical behavior of (2.8) changes from steady-state to chaotic, with the increase of r.

    Figure 1 depicts the bifurcation diagram when the parameters values σ=10,b=83 and d=193 are fixed, and r varies on the interval [0,100]. In fact, one can see that chaos occurs after r>42.5. For the value of the parameters σ=10,b=83,d=193,r=43 and r=25, Lyapunov exponents are shown in Figure 2. The values of Lyapunov exponents at 500th second are L1=1.1281, L2=0.0073, L3=1.3786, L4=1.3774 and L5=1.6688. It is easy to observe that if r=43, then system (2.8) has the positive largest Lyapunov exponent. Therefore, the system (2.8) can exhibit chaotic behaviors. When selecting parameters σ=10,b=83,r=25 and d=193, the values of Lyapunov exponents at 500th second are L1=0.5246, L2=0.5358, L3=6.9952, L4=2.0305 and L5=4.1633. All negative Lyapunov exponents indicate that the behavior of the system is non-chaotic.

    Figure 1.  Bifurcation diagram of (2.8) with σ=10,b=83, d=193 and varying r.
    Figure 2.  Lyapunov exponent spectra for system (2.8) with σ=10,b=83,d=193,r=43 and r=25.

    The time responses of the system for r=25 and r=43 are depicted in Figure 3. We verify that the system with r=25 produces a steady-state solution and when r=43 the system is in a chaotic state. Figure 4 depicts the phase portraits of system (2.8) when σ=10,b=83,r=25 and d=193. Figure 5 shows its chaotic behavior with σ=10,b=83,r=43 and d=193.

    Figure 3.  State trajectories of (2.8) with σ=10,b=83,d=193,r=25 and r=43.
    Figure 4.  The phase portraits of (2.8) with σ=10,b=83,r=25 and d=193.
    Figure 5.  Visualization of the chaotic attractor of (2.8) with σ=10,b=83,r=43 and d=193.

    In this section, the Hamilton energy for the 5-dimensional Lorenz model (5DLM) is investigated. The Hamilton energy plays a crucial role in the stability of dynamical systems [43]. By continuously pumping or releasing energy in the system, we are able to stabilize chaos. Furthermore, the relation between the Hamilton energy and different chaotic attractors of system (2.8) and the energy dependence on attractors are discussed. Calculation of Hamilton energy for high-order Lorenz systems including six-dimensional Lorenz model (6DLM) [18], seven-dimensional Lorenz model (7DLM) [19] and generalized Lorenz model (GLM) [20], can be an interesting topic due to their special physical nature.

    Let us rewrite (2.8) in the form:

    [˙x1˙x2˙x3˙x4˙x5]=Fc+Fd=[σx2rx1x1x3x1x2x1x4x1x32x1x52x1x4]+[σx1x2bx3dx44bx5]
    =[0σ000σ0x1000x10x1000x102x10002x10][rx1σx2x3x4x5]+[σ2r00000100000b00000d000004b][rx1σx2x3x4x5]
    =J(X)H+R(X)H,

    where H stands for the gradient vector of a smooth energy function H(X), J(X) represents a skew-symmetric matrix, and R(X) denotes a symmetric matrix.

    The Hamilton energy function can be expressed as:

    dHdt=HTR(X)H, (2.9)
    HTJ(X)H=0. (2.10)

    Using the Helmholtz's theorem [43], it can be represented by:

    HTFc(X)=0, (2.11)
    HTFd(X)=dHdt. (2.12)

    Thus, we have:

    H=12rσx21+12x22+12x23+12x24+12x25. (2.13)

    Furthermore, it rate of variation is:

    dHdt=rx21x22bx23dx244bx25. (2.14)

    Figure 6 illustrates that chaotic and steady-state require lower and higher Hamilton energy, respectively.

    Figure 6.  The Hamilton energy function H, with σ=10,b=83,d=193,r=25 and r=43.

    In this section, we pursue the goal of proving the existence of GEAS for the chaotic system (2.8). We will first mention a few prerequisites and definitions.

    Let X=(x1,x2,x3,x4,x5)T, and consider that X(t,t0,X0) is the solution of system

    dXdt=g(X), (3.1)

    that satisfies X0=X(t0,t0,X0), with t00 representing the initial time. Also, g:R5R5 and ΨR5 is a compact set. Let us define the distance between X(t,t0,X0) and Ψ as:

    ρ(X(t,t0,X0),Ψ)=infZΨX(t,t0,X0)Z. (3.2)

    Denote Ψγ={Xρ(X,Ψ)<γ}. Thus, one gets ΨΨγ.

    Definition 3.1. [21]. Suppose that ΨR5 is a compact set. If for any X0R5/Ψ,

    limtρ(X(t),Ψ)=0,

    then Ψ is an UBS of (3.1). Moreover, if for any X0Ψ and all tt0, X(t,t0,X0)Ψ, then Ψ is the PIS for (3.1).

    From the above, it is interpreted that having a GEAS for a system guarantees that the system is UBS.

    Definition 3.2. [21]. Given a Lyapunov function Lγ(X), if there exist constants Kγ>0 and sγ>0, such that

    X0R5,Lγ(X(t))Kγ)(Lγ(X0)Kγ)esγ(tt0), (3.3)

    for Lγ(X0)>Kγ and Lγ(X)>Kγ, then Ψγ={X|Lγ(X(t))Kγ} is a GEAS of (2.8). Moreover, if for any X0Ψγ and all t>t0, X(t,t0,X0)Ψγ, then Ψγ is a PIS.

    The next theorem introduces the GEAS for system (2.8).

    Theorem 3.1. For any σ>0, b>0, r>0,d>0, β>0 and α>0, with

    Lα,β(X(t))=αx21+βx22+βx24+β(x3σα+rββ)2+β(x5σα+rβ2β)2, (3.4)
    ϵ=min{1,σ,b,d}>0,Kα,β=11b7βϵ(σα+rβ)2>0,X(t)=(x1(t),x2(t),x3(t),x4(t),x5(t)),X(t0)=(x1(t0),x2(t0),x3(t0),x4(t0),x5(t0)).

    If Lα,β(X(t))Kα,β,tt0, then

    Lα,β(X(t))Kα,β[Lα,β(X0)Kα,β]eϵ(tt0).

    This indicates that

    Ψα,β(X(t))={X|αx21+βx22+βx24+β(x3σα+rββ)2+β(x5σα+rβ2β)2Kα,β}

    is a GEAS and PIS of the system.

    Proof. Let us define

    f(x5)=7bβx25+3b(σα+rβ)x5, (3.5)

    and consider the definite positive Lyapunov function

    Lα,β(X(t))=αx21+βx22+β(x3σα+rββ)2+βx24+β(x5σα+rβ2β)2. (3.6)

    The derivative of Lα,β is as follows

    dLα,βdt=2αx1˙x1+2βx2˙x2+2β(x3σα+rββ)˙x3+2βx4˙x4+2β(x5σα+rβ2β)˙x5=2αx1(σx2σx1)+2βx2(x1x3+rx1x2)+2β(x3σα+rββ)(x1x2x1x4bx3)+2βx4(x1x32x1x5dx4)+2β(x5σα+rβ2β)(2x1x44bx5)=σαx21βx22βb(x3σα+rββ)2βdx24bβ(x5σα+rβ2β)2σαx21βx22bβx23βdx247bβx25+5b4β(σα+rβ)2+3b(σα+rβ)x5.

    From (3.5) we have

    maxxRf(x5)=9b28β(σα+rβ)2, (3.7)

    therefore,

    dLα,βdtσαx21βx22βb(x3σα+rββ)2βdx24bβ(x5σα+rβ2β)2+11b7β(σα+rβ)2ϵLα,β(X(t))+ϵKα,β<0, (3.8)

    when Lα,β(X(t))Kα,β. Then, we have the equivalent

    Lα,β(X(t))Lα,β(X(t0))eϵ(tt0)+tt0ϵeϵ(tτ)Kα,βdτ=Lα,β(X(t0))eϵ(tt0)+Kα,β(1eϵ(tt0)).

    Thus, if Lα,β(X(t))Kα,β,tt0, the following inequality results

    Lα,β(X(t))Kα,β[Lα,β(X(t0))Kα,β]eϵ(tt0).

    By calculating the limit, one has

    ¯limtLα,β(X(t))Kα,β.

    Therefore, the ellipsoid

    Ψα,β(X(t))={X|αx21+βx22+βx24+β(x3σα+rββ)2+β(x5σα+rβ2β)2Kα,β}

    is the GEAS and PIS of system (2.8). This ends the proof.

    Different cases can be highlighted:

    Case 1: For α=1,β=1, then

    Ψ1,1={X|x21+x22+(x3(σ+r))2+x24+(x5σ+r2)211b7ϵ(σ+r)2},

    is the GEAS of (2.8). Fore σ=10,b=83,r=25 and d=193, it yields

    Ψ1,1={X|x21+x22+(x335)2+x24+(x5352)271.62}.

    Figure 7 illustrates the attractors of (2.8) in distinct spaces by Ψ1,1. For σ=10,b=83,r=43 and d=193, results

    Ψ1,1={X|x21+x22+(x353)2+x24+(x5532)2108.42}.
    Figure 7.  Phase portraits and GEAS of (2.8) with σ=10,b=83,r=25 and d=193.

    Figure 8 shows the attractors in different spaces defined by Ψ1,1.

    Figure 8.  Phase portraits and GEAS of (2.8) with σ=10,b=83,r=43 and d=193.

    Case 2: Let us consider α=1,β=2. Thus, the set

    Ψ1,2={X|x21+2x22+2(x3a+2r2)2+2x24+2(x5σ+2r4)211b14ϵ(σ+2r)2},

    is the GEAS of (2.8).

    For σ=10,b=83,r=25 and d=193, we have

    Ψ1,2={(x1,x2,x3,x4,x5)|x21+2x22+2(x330)2+2x24+2(x515)286.82}.

    Case 3: Define α=2,β=1. Then,

    Ψ2,1={X|2x21+x22+(x3(2σ+r))2+x24+(x52σ+r2)211b7ϵ(2σ+r)2},

    is the GEAS of (2.8).

    For σ=10,b=83,r=25 and d=193, one has

    Ψ2,1={X|2x21+x22+(x345)2+x24+(x522.5)292.12}.

    In this section we derive a more accurate and smaller boundary set than that established by Theorem 3.1. We state the following theorem.

    Theorem 4.1. If σ>0,b>0,r>0 and d>0, then we have the following boundaries for system (2.8) variables:

    |x1|211b7σr, (4.1)
    |x2|11b7r, (4.2)
    |x3ηβ|11b7r, (4.3)
    |x4|11b7r, (4.4)
    |x5η2β|11b7r, (4.5)

    where

    η=σα+rββ.

    Proof. According to the results obtained from Theorem 3.1, we have

    |x1|11b7ηα,|x2|11b7ηβ,|x3ηβ|11b7ηβ,|x4|11b7ηβ,|x5η2β|11b7ηβ.

    It is clear from the above equations that the upper bound of x1,x2,,x5, depends on the lower bound of ηα and ηβ :

    ηα=σαβ+rβα2σr.

    Therefore, one can determine the bound for x1 as shown in Eq (4.1). For variables x2,x3,x4 and x5, there is the same term:

    ηβ=σαβ+r.

    Let us take αβ=1N with NN. Therefore,

    11b7ηβ=(1Nσ+r)11b7.

    Since

    N=1{x2R||x2|(1Nσ+r)11b7}={x2R||x2|11b7r},

    we find a more limited boundary set for x2 as shown in Eq (4.2), and by doing the same process one can obtain

    N=1{x3R||x3ηβ|(1Nσ+r)11b7}={x3R||x3ηβ|11b7r},N=1{x4R||x4|(1Nσ+r)11b7}={x4R||x4|11b7r},N=1{x5R||x5η2β|(1Nσ+r)11b7}={x5R||x5η2β|11b7r}.

    To confirm the theoretical results, we fix σ=10,b=83,r=25 and d=193. Figure 9, shows the estimated bounds for each state variable. Furthermore, Table 2 compares the bounds estimated by Theorems 3.1 and 4.1, showing the advantage of Theorem 4.1.

    Figure 9.  The phase portraits and GEAS of (2.8) with σ=10,b=83,r=25 and d=193.
    Table 2.  The bounds for system (2.8).
    Theorem 3.1 Theorem 4.1 Numerical results
    x1 (-71.6, 71.6) (-64.5, 64.5) (-18.3, 19.7)
    x2 (-71.6, 71.6) (-51.1, 51.1) (-23.4, 25.9)
    x3 (-36.6,106.6) (-16.1, 86.1) (0.93, 37.6)
    x4 (-71.6, 71.6) (-51.1, 51.1) (-7.73, 8.98)
    x5 (-54.1, 89.1) (-33.6, 68.6) (0.06, 19.6)

     | Show Table
    DownLoad: CSV

    Remark 4.1. A noteworthy point in calculating the bound for system (2.8) based on Theorem 4.1 is that the estimated boundary, in addition to being smaller, is independent of parameters β and α.

    This section addresses the finite time synchronization of the 5DLM. In order to achieve fast and reliable synchronization, we use the results obtained in the previous section for the ultimate bound of (2.8). Let us consider the 5DLM (2.8) as the master, and define the slave by:

    ˙y1=σ(y2y1)+u1,˙y2=y1y3+ry1y2+u2,˙y3=y1y2y1y4by3+u3,˙y4=y1y32y1y5dy4+u4,˙y5=2y1y44by5+u5, (5.1)

    where y1,y2,y3,y4,y5 are state vectors. The control law u1,u2,u3,u4,u5 is designed for the drive (2.8) and response (5.1) systems can reach synchronization in finite time.

    Lemma 5.1. Suppose that a1,a2,,an are real numbers and that 0<s<1. Then, we have:

    (ni=1ai)sni=1asi.

    Lemma 5.2. Inequality 2abϵa2+1ϵb2 holds for all real numbers ϵ>0,a>0 and b>0.

    Lemma 5.3. Let us assume that L(t) is a continuous and positive-definite function that satisfies:

    ˙L(t)λL(t)μLω(t),t0,L(0)>0. (5.2)

    Then, the system is exponentially finite-time stable, where λ,μ>0, and 0<ω<1 are constants.

    Lemma 5.4. Let us suppose that L(t) is a Lyapunov function satisfying Eq. (5.2). Then, it holds

    L1ω(t)(μλ)+eln[λL1ω(0)+μ]λ(1ω)(t0)λ,0tT

    and L(t)=0,tT. The finite time T is

    T=ln(λL1ω(0)μ+1)λ(1ω). (5.3)

    Definition 5.1. Let us consider δ(t)=[δ1(t)δ2(t)δ3(t)δ4(t)δ5(t)]T, and define δi(t)=yi(t)xi(t) as the synchronization errors. If there exists a positive value T such that

    limtTδ(t)∥=0, (5.4)

    and δ(t)∥=0, for tT, then the systems (2.8) and (5.1), achieve finite-time synchronization.

    The next theorem states the exponential finite-time synchronization condition of systems (2.8) and (5.1).

    Theorem 5.1. The systems (2.8) and (5.1) can achieve finite-time synchronization by the control law:

    {u1(t)=(λ+522b7r+2(σ+r))δ1(t)μδs1(t),u2(t)=(λ+1211b14r)δ2(t)μδs2(t),u3(t)=(λ+11b14r)δ3(t)μδs3(t),u4(t)=(λ+5211b14r+(σ+r)2)δ4(t)μδs4(t),u5(t)=(λ+11b14r)δ5(t)μδs5(t), (5.5)

    where λ,μ>0, and s(0,1). Further, the systems are synchronized in the time

    T=ln(λL2(1ω)(0)μ+1)λ(1ω), (5.6)

    where ω=s+12.

    Proof. Theorem 4.1 provides an accurate estimate of the ultimate bound of variables of system (2.8). Let consider α=β=1, then η=σ+r. Therefore, we have

    |x1|211b7σr,|x2|11b7r,|x3(σ+r)|11b7r,|x4|11b7r,|x5σ+r2|11b7r. (5.7)

    From (2.8) and (5.1), the following error dynamics results:

    ˙δ1=σ(δ2δ1)+u1,˙δ2=y1y3+ry1y2+u2+x1x3rx1+x2=(δ1+x1)(δ3+x3)+r(δ1+x1)(δ2+x2)+u2+x1x3rx1+x2=δ1δ3δ1x3δ3x1+rδ1δ2+u2,˙δ3=y1y2y1y4by3+u3x1x2+x1x4+bx3=(δ1+x1)(δ2+x2)(δ1+x1)(δ4+x4)b(δ3+x3)+u3x1x2+x1x4+bx3=δ1δ2+δ1x2+δ2x1δ1δ4δ1x4δ4x1bδ3+u3,˙δ4=y1y32y1y5dy4+u4x1x3+2x1x5+dx4=(δ1+x1)(δ3+x3)2(δ1+x1)(δ5+x5)d(δ4+x4)+u4x1x3+2x1x5+dx4=δ1δ3+δ1x3+δ3x12δ1δ52δ1x52δ5x1dδ4+u4,˙δ5=2y1y44by5+u52x1x4+4bx5=2(δ1+x1)(δ4+x4)4b(δ5+x5)+u52x1x4+4bx5=2x1δ4+2δ1x4+2δ1δ44bδ5+u5. (5.8)

    Let us consider the Lyapunov function:

    L1(t)=δ21(t)+δ22(t)+δ23(t)+δ24(t)+δ25(t).

    The time domain derivative of L(t) along the trajectories of (5.8) and using control law (5.5) is given by

    dL1(t)dt=2δ1˙δ1+2δ2˙δ2+2δ3˙δ3+2δ4˙δ4+2δ5˙δ5=2δ1(σδ2σδ1+u1)+2δ2(δ1δ3δ1x3δ3x1+rδ1δ2+u2)+2δ3(δ1δ2+δ1x2+δ2x1δ1δ4δ1x4δ4x1bδ3+u3)+2δ4(δ1δ3+δ1x3+δ3x12δ1δ52δ1x52δ5x1dδ4+u4)+2δ5(2x1δ4+2δ1x4+2δ1δ44bδ5+u5)=2σδ212δ222bδ232dδ248bδ25+2(σ+rx3)δ1δ2+2δ1δ3x22δ1δ3x4+2δ1δ4x34δ1δ4x5+4δ1δ5x4+2δ1u1+2δ2u2+2δ3u3+2δ4u4+2δ5u52|σ+rx3||δ1||δ2|+2|δ1||δ3||x2|+2|δ1||δ3||x4|+2|δ1||δ4||x3|+4|δ1||δ4||x5|+4|δ1||δ5||x4|+2δ1u1+2δ2u2+2δ3u3+2δ4u4+2δ5u5211b7r|δ1||δ2|+211b7r|δ1||δ3|+211b7r|δ1||δ3|+2(11b7r+σ+r)|δ1||δ4|+4(11b7r+σ+r2)|δ1||δ4|+411b7r|δ1||δ4|+411b7r|δ1||δ5|
    +2δ1[(λ+522b7r+2(σ+r))δ1(t)μδs1(t)]+2δ2[(λ+1211b14r)δ2(t)μδs2(t)]+2δ3[(λ+11b14r)δ3(t)μδs3(t)]+2δ4[(λ+5211b14r+(σ+r)2)δ4(t)μδs4(t)]+2δ5[(λ+11b14r)δ5(t)μδs5(t)].

    From Lemma 5.2, we have

    2|δ1||δ2|2δ21+12δ22,2|δ1||δ3|2δ21+12δ23,2|δ1||δ4|2δ21+12δ24,2|δ1||δ5|2δ21+12δ25. (5.9)

    Then using Lemma 5.3, we obtain

    dL1(t)dt2λδ212λδ222λδ232λδ242λδ252μ(δs+11+δs+12+δs+13+δs+14+δs+15)2λ(δ21+δ22+δ23+δ24+δ25)2μ(δ21+δ22+δ23+δ24+δ25)1+s2=2λL(t)2μLω(t),t0,

    where λ,μ>0,ω=1+s2. This implies that

    e2λtdL1(t)dt+2λe2λtL1(t)=ddt(e2λtL1(t))0.

    Therefore,

    t0ddt(e2λtL1(t))dt=e2λtL1(t)L1(0)0,

    which leads to

    L1(t)e2λtL1(0),

    and since δ(t)2=L1(t), we obtain

    δ(t)∥≤L1(0)eλt,t0.

    This, from Lemma 5.3 and Lemma 5.4, guarantees the exponential synchronization of systems (2.8) and (5.1) in finite time T.

    To be more realistic, an approach is presented here that requires only one controller to implement synchronization.

    Corollary 5.1. When the control functions are chosen as

    u1=(λ+522b7r+2(σ+r))δ1(λ+1211b14r)δ22δ1(λ+11b14r)δ23δ1(λ+5211b14r+(σ+r)2)δ24δ1(λ+11b14r)δ25δ1μδs1μδs+12δ1μδs+13δ1μδs+14δ1μδs+15δ1,u2=u3=u4=u5=0, (5.10)

    then, the drive system (2.8) is exponentially synchronized with the slave system (5.1).

    Proof. The Lyapunov function is selected as

    L(t)=δ21(t)+δ22(t)+δ23(t)+δ24(t)+δ25(t).

    In view of (5.8) and (5.10), the derivative of L(t) is

    dL1(t)dt=2σδ212δ222bδ232dδ248bδ25+2(σ+rx3)δ1δ2+2δ1δ3x22δ1δ3x4+2δ1δ4x34δ1δ4x5+4δ1δ5x4+2δ1u12|σ+rx3||δ1||δ2|+2|δ1||δ3||x2|+2|δ1||δ3||x4|+2|δ1||δ4||x3|+4|δ1||δ4||x5|+4|δ1||δ5||x4|+2δ1u1211b7r|δ1||δ2|+211b7r|δ1||δ3|+211b7r|δ1||δ3|+2(11b7r+σ+r)|δ1||δ4|+4(11b7r+σ+r2)|δ1||δ4|+411b7r|δ1||δ4|+411b7r|δ1||δ5|2(λ+522b7r+2(σ+r))δ21
    2(λ+1211b14r)δ222(λ+11b14r)δ232(λ+5211b14r+(σ+r)2)δ242(λ+11b14r)δ252μδs12μδs+122μδs+132μδs+142μδs+15.

    According to Lemma 5.2, Lemma 5.3 and (5.9) we have

    dL1(t)dt2λδ212λδ222λδ232λδ242λδ252μ(δs+11+δs+12+δs+13+δs+14+δs+15)2λ(δ21+δ22+δ23+δ24+δ25)2μ(δ21+δ22+δ23+δ24+δ25)1+s2=2λL(t)2μLω(t),t0,

    where λ,μ>0,ω=1+s2. Therefore, synchronization is achieved exponentially with the control law (5.10).

    To show the effectiveness of the control proposed in theorem 5.1, we perform numerical simulations. Let us choose the initial conditions x1(0)=1,x2(0)=1,x3(0)=2,x4(0)=1,x5(0)=1, y1(0)=0.1,y2(0)=0.1,y3(0)=0.2,y4(0)=1,y5(0)=1, and other parameters as λ=1, μ=1 and s=13. Figure 10 depicts the time series of the drive system (2.8) and the response system (5.1) without input control, in which the goal of synchronization has not been achieved. Figure 11 shows the case of using the proposed control (5.5). We verify that system (5.1) exponentially synchronizes with the master system (2.8) within the guaranteed convergence time.

    Figure 10.  State trajectories of the drive and response systems without control input.
    Figure 11.  State trajectories of the drive and response systems with one control input.

    The synchronization error for different modes with different controllers are depicted in Figure 12. The noteworthy point in these figures is that the goal of synchronization is achieved faster by increasing the number of controllers.

    Figure 12.  Synchronization errors with different control inputs.

    The global dynamics of the 5DLM, which was obtained by increasing two modes to the original Lorenz system, was analyzed. Phase portraits, bifurcation diagrams and GEAS were estimated. Due to the dependence of the GEAS on the free parameters, a new boundary for the variables was estimated, which is more accurate than the existing one. Also, by employing a finite time control scheme, a synchronization method was proposed based on the obtained ultimate bound sets. The corresponding boundedness was numerically verified to demonstrate the efficiency of the presented method.

    The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.

    The authors declare that there are no conflicts of interests regarding the publication of this article.



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