# An Improved Linear State Error Feedback Synchronization Control Criteria for a Six-Axis Duffing Chaotic System

Authors: Hao Jia; Chen Guo
Journal: Mathematical Problems in Engineering
 (2021)

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## Abstract

By the employment of an improved linear state error feedback method, the synchronization control of a six-axis Duffing chaotic system was studied. Compared with previous methods, it has two advantages: the nonlinear term of the Duffing chaotic system is reserved in the synchronization error system, and the trajectory bound of the response system is predicted in advance to deduce the synchronization criteria. First, a typical ship parametric excitation roll chaotic system with parametric and forced excitation is taken as the control object to realize chaos synchronization control. Then, the control method is applied to the conventional six-axis Duffing oscillatory chaotic system and the four-axis Duffing oscillatory chaotic system. Finally, three simulation cases are presented to illustrate the validity of the synchronization criteria.


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## Body

## 1. Introduction

Some dampers and drive model oscillators have been widely used in ship, machinery, and electrical systems, among which a typical one is the six-axis Duffing oscillator [1–5]. With proper parameters, a Duffing oscillator exhibits chaotic behaviors. Chaos is an important branch of nonlinear science and a complex dynamic behavior. Chaos synchronization has been the focus of present research on chaos. Chaos synchronization has potential applications in many fields, such as secure communication in the field of communication, automatic control in aerospace, roll control in ship navigation, and electrical chaos control in power systems. Many scholars have studied its chaos synchronization through many methods, including adaptive pulse control [6], optimal control [7], back stepping [8], pure finite time control [9], sliding mode control [10, 11], PID control [12], linear state feedback control [13], and fuzzy logic control [14]. In most methods, however, the nonlinear terms intrinsically contained by the system are removed from the controller. For example, in prior studies [4, 5], Njah employed active control to realize master-slave system synchronization.In the present work, a parametric excitation roll motion system of a ship is first taken as the object to study the synchronization problem of six-axis Duffing chaos systems. The mathematical model of the parametric excitation rolling motion system of ship is a typical six-axis Duffing chaos system. So far, many different synchronization control techniques of chaos systems have been applied to ship roll systems, such as conventional passivity-based control, random Melnikov method [15], negative feedback algorithm [16], and chaos search with ant colony algorithm [17]. Other research on the roll chaos of ship has also been conducted, such as researching a numerical approach of the chaos threshold [18], researching the rolling vibration of a nonlinear semisubmersible system using an improved IHBC algorithm [19], and analyzing the environmental risk and ultimate roll motion of a ship [20]. It should be noted that in six-axis Duffing chaos systems, the chaotic oscillator is a nonlinear system, where nonlinear terms play an important role in the generation of the chaotic attractor, and thus how to use the properties of the nonlinear error system to derive synchronization conditions is very meaningful. Moreover, the trajectory boundary of the drive system and the response system is broadly applied to the derivation of the criteria of synchronization chaos system (e.g., prior work by authors in [21–24]), but the estimation of the response system boundary is difficult. Therefore, it is more meaningful to solve some (not all) of the bounds of the trajectory of the response system in advance of the drive system and to realize synchronization criteria by the estimated bounds of the response system.In this work, a synchronization scheme tailored for the parametric excitation roll chaos system of a ship with parametric and forced excitation is constructed, and linear state error feedback control is employed. Firstly, the trajectory bound of the response system is found, and then this is utilized to obtain synchronization conditions while the nonlinear terms of the system are retained in the synchronization scheme to ensure the high efficiency of control criteria. Besides, three typical Duffing oscillatory chaos systems are used to illustrate the applicability of the synchronization criteria.The remainder of this work is organized as follows: In section2, relevant issues and concepts are introduced. In section 3, the synchronization criteria tailored for the parametric excitation roll six-axis Duffing chaos system of a ship with parametric and forced excitation are presented. In section 4, the applications of synchronization criteria of a classical six-axis Duffing oscillatory chaos system and a four-axis Duffing oscillatory chaos system are presented. In section 5, the simulation results of the parametric excitation roll Duffing chaos system of a ship with parametric and forced excitation, a classical six-axis Duffing oscillatory chaos system, and a four-axis Duffing oscillatory chaos system are presented. The conclusions and future work are discussed in section 6.


## 2. Problems Proposed

The nonlinear mathematical model for the roll system of a ship with parametric and forced excitation in a regular longitudinal wave is as follows:(1)ϕ¨t+μ1ϕ˙t+μ3ϕ˙3t+ω02ϕt+α3ϕ3t+α5ϕ5t+h0ϕtcosω1t,=Kesinω1t+δ0,where ϕt is the roll angle; h0 is the amplitude of parametric excitation; ω1 is the frequency of metric excitation, which is usually twice that of natural frequency when chaos occurs in the system; μ1 and μ3 are the damping factors of the roll; α3 and α5 are the dimensionless righting moment coefficients; ω0 is the natural frequency of the ship roll; and Kesinω1t+δ0 is the forced roll moment in regular waves, where Ke is the amplitude of the forced excitation and δ0 is encounter location of the ship and regular wave, assigned as 0. The potential term in the solution of the equation above is(2)12ω02ϕ2t+14ω02α3ϕ4t+16ω02α5ϕ6t,and hence it is called a six-axis system.Letx1t=ϕt,x2t=ϕ˙t, and, by conversion to state equations, it is obtained as follows:(3)x˙1t=x2t,x˙2t=−ax2t−bx23t−cx1t+dx13t−ex15t−fx1tcosωt+gsinωt,wherea=μ1,b=μ3,c=ω02,d=−ω02α3, e=ω02α5,f=ω02h0,ω=ω1,andg=Ke.To reflect the actual states more accurately, relevant parameters can be assigned with the reference of a marine patrol vessel as an example as follows:(4)a=0.069,b=0.5367,c=1.25,d=0.9926,e=0.0998,f=2.875,ω=2.236,g=0.16.As the parameters are determined, the nonlinear mathematical model for the parametric excitation roll system of the ship is as follows:(5)x˙1t=x2t,x˙2t=−0.069x2t−0.5367x23t−1.25x1t+0.9926x13t−0.0998x15t−2.875x1tcos2.236t+0.16sin2.236t.The parametric excitation roll system of a ship is a chaos system that can be proved by MATLAB simulation results. The history charts ofx1 and x2 of the parametric excitation roll system of the ship are shown in Figures 1 and 2, respectively, in which it is clear that the system is in chaos. The phase diagram of the parametric excitation roll system of the ship is shown in Figure 3, which further proves that the system is a chaos system and like a Duffing chaos system. Hence, this system is a typical six-axis Duffing chaos system. The next step is to perform synchronization control for system (3).Figure 1
Time response ofx1for system (5).Figure 2
Time response ofx2for system (5).Figure 3
Phase diagram of chaotic system (5).It is obtained from equation (3) that(6)x˙1t=x2t,x˙2t=−c+fcosωtx1t−ax2t+ix1t+jx2t+qt,where(7)ix1t=dx13t−ex15t,(8)jx2t=−bx23t,and(9)qt=gsinωt.System (6) is defined as a drive system, and the initial values of variables of system (6) are defined as x10=x10,x20=x20. The response system is as follows:(10)y˙1t=y2t+u1t,y˙2t=−c+fcosωty1t−ay2t+iy1t+jy2t+qt+u2t,where(11)iy1t=dy13t−ey15t,and(12)jy2t=−by23t.The initial values of the variables of system (10) are defined as y10=y10,y20=y20. The error system is assumed to be(13)et=e1te2t∈R2.In this work, bold type denotes a vector. In the equation above,(14)e1t=x1t−y1t,e2t=x2t−y2t.The controller is selected as(15)u1t=k1e1t+e2t,u2t=k2e1t+k3e2t,where k1, k2, and k3 are controller gains which can be determined later. According to the method in this paper, k1, k2, and k3 and other system parameters constitute the constraints of system synchronization, and k1, k2, and k3 should ensure that other system parameter values meet the actual situation.Then, from equations (6), (10), (14), and (15), the following is obtained:(16)e˙1t=−k1e1t,e˙2t=−c+fcosωt−k2e1t−k3+ae2t+ix1t−iy1t+jx2t−jy2t.With the differential mean value theorem and equations (7) and (11), it is obtained that(17)ix1t−iy1t=i˙γ1tx1t−y1t=i˙γ1te1t,where(18)i˙γ1t=diρdρ|ρ=γ1t=3dγ12t−5eγ14t,and γ1t∈minx1t,y1t,maxx1t,y1t.Similarly, by deriving from equations (8) and (12), it is obtained that(19)jx2t−jy2t=j˙γ2tx2t−y2t=j˙γ2te2t,where(20)j˙γ2t=djρdρ|ρ=ξ2t=−3bγ22t,and γ2t∈minx2t,y2t,maxx2t,y2t.By substituting equations (17) and (19) into equation (16), it is obtained that(21)e˙1t=−k1e1t,e˙2t=−c+fcosωt−k2+i˙γ1te1t+−k3+a+j˙γ2te2t.Then,(22)e˙t=k^tet,where(23)k^t=k11k12k21k22,k11=−k1,k12=0,k21=−c+fcosωt−k2+i˙γ1t,k22=−k3+a+j˙γ2t.The initial conditions of system (21) are e10=x10−y10,e20=x20−y20.Because the drive system described by equation (6) is chaotic, there are two boundary values n1>0,n2>0, and for the initial values x10,x20 in any chaotic attractor region range formed in Figure 3, the following equation is satisfied:(24)xit≤ni,i=1,2,∀t>0,e1t=x10−y10exp−k1t,y1t=x1t−x10−y10exp−k1t,and, finally,(25)y1t≤x1t+x10−y10≤n1+x10−y10,∀t>0.Similarly,(26)y2t≤x2t+x20−y20≤n2+x20−y20.It is obtained from equation (11) that(27)i˙γ1t=3dγ12t−5eγ14t≤3dγ12t+5eγ14t≤λ1,where(28)λ1=3d+5eα1α1,and(29)α1=maxγ12t.∀γ1t∈minx1t,y1t,maxx1t,y1t, similarly,(30)j˙γ2t≤λ2,where(31)λ2=3bα2,and(32)α2=maxγ22t,∀γ2t∈minx2t,y2t,maxx2t,y2t.Note 1. As to ∀t>0, the critical values of y1t can be estimated by equation (25) and those of y2t can be estimated by equation (26); the critical values of i˙γ1t and λ1 can be estimated by equations (27), (28), and (29), and those of j˙γ2t and λ2 can be estimated by equations (30), (31), and (32).The objective of this work is to study the master-slave synchronization of the system described by system (6); solve the gains of controller k1,k2,k3 and make the error system described by equation (21) tend to be globally stable to realize synchronization of the systems described by systems (6) and (10), that is, realize synchronization of the roll six-axis Duffing chaos system of a ship with parametric and forced excitation.


## 3. Synchronization Criteria

In this section, the stability criteria for error system (16) are given to ensure the synchronization of systems (6) and (10).Theorem 1.
If the Lyapunov function is selected as follows:(33)Vt=eTtQet,Q=q11q12q21q22∈R2×2,and the following criteria are satisfied:(34)Φ1=−k1q11+−c+fcosωt−k2q12+λ1⋅q12<0;Φ2=−k3+aq22+λ2q22<0−k1−k3−aq12+−c+fcosωt−k2q22+λ1q22+λ2q122≤4Φ1Φ2,then, with the control of error system (16) and controller (15), systems (6) and (10) are synchronized.Proof.
(35)V˙t=eTtMtet,Where,Mt=k^TtQ+Qk^t=m11tm12tm21tm22t.
By substitutingk^t and Q into Mt in the equation above, the following can be obtained:(36)m11t=2−k1q11+−c+fcosωt−k2+i˙γ1tq12;m12t=−k1q12+−c+fcosωt−k2+i˙γ1tq22+−k3+a+j˙γ2tq12;m22t=2−k3+a+j˙γ2tq22.
If these are satisfied,(37)m11t<0,m22t<0,m122t<m11tm22t,then V˙t<0,∀e1t,e2t≠0, and thus systems (6) and (10) are synchronized, and the conclusion is proved. Next, the relationship between equations (37) and (34) is proved.
From the first equation in equation (36), it is obtained that(38)m11t2=−k1q11+−c+fcosωt−k2q12+i˙γ1tq12≤−k1q11+−c+fcosωt−k2q12+i˙γ1tq12.
By substituting equation (27) into the equation above, it is obtained that(39)m11t2≤−k1q11+−c+fcosωt−k2q12+λ1⋅q12=Φ1.
The second equation in equation (36) yields(40)m22t2=−k3+a+j˙γ2tq22=−k3+aq22+j˙γ2tq22≤−k3+aq22+j˙γ2tq22.
By substituting equation (30) into the equation above, it is obtained that(41)m22t2≤−k3+aq22+λ2q22=Φ2.
The third equation in equation (36) yields(42)m122t=−k1q12+−c+fcosωt−k2+i˙γ1tq22+−k3+a+j˙γ2tq122=−k1q12+−c+fcosωt−k2q22+i˙γ1tq22+−k3+aq12+j˙γ2tq122=−k1−k3−aq12+−c+fcosωt−k2q22+i˙γ1tq22+j˙γ2tq122≤−k1−k3−aq12+−c+fcosωt−k2q22+i˙γ1tq22+j˙γ2tq122.
By substituting equations (27) and (30) into the equation above, the following is obtained:(43)m122t≤−k1−k3−aq12+−c+fcosωt−k2q22+λ1q22+λ2q122.
In sum, if equation (34) is true, then equation (37) can be guaranteed to be true, and, at this point, error system (16) achieves global asymptotic stability, and thus systems (6) and (10) reach synchronization with the control of controller (15). The proof is concluded, that is, the synchronization criteria of equation (34) are obtained for systems (6) and (10) to reach synchronization.
Generally, letQ=1001, and then, when systems (6) and (10) reach synchronization, the synchronization criteria equations become(44)k1>0;−k3−a+λ2<0;2k1k3+a−λ2>c+fcosωt+k2+λ1.Note 1.
In prior works [4, 5], Njah established a synchronization scheme for the six-axis Duffing equation and studied the master-slave synchronization criteria with active control, which are as follows:Controller:(45)u1t=−a^e1t−b^e2t,u2t=−ax15t−y15t−lx13t−y13t−w1e1t−w2e2t,wherea^,b^,w1,w2 are the input parameters of the controller.Error system:(46)e˙1t=a^e1t+1+b^e2t,e˙2t=−d−w1e1t−c−w2e2t.
It is clear that the linear error system described by (46) can be completely derived by controller (45) [4, 5], but all the nonlinear terms of the original six-axis Duffing system are removed, and this error system can be easily acquired. Compared with controller (45) [4, 5], controller (15) and error system (21) in the present work have two merits. The first is the reservation of nonlinear terms in error system (21) instead of linear error system (46). The second is easy preestimation of the bounds of the response systems y1t,y2t by equations (24)–(32) and the subsequently obtained stability criteria for the error system described by (16).


## 4. Applications of the Six-Axis Duffing Oscillatory System and the Four-Axis Duffing Oscillatory System

### 4.1. Application of the Six-Axis Duffing Oscillatory System

In the previous section, the synchronization criteria for the six-axis Duffing oscillatory system in prior work [4, 5] was compared with that for the parametric excitation roll system of a ship in this work, but the method proposed in this work has not been applied to a conventional six-axis Duffing oscillatory system, which is to be presented here.The mathematical model of the six-axis Duffing oscillatory system is as follows:(47)x¨t=−cx˙t−dxt−lx3t−ax5t+qcosωtt.Letx1t=xt,x2t=x˙1t, then equation (47) becomes(48)x˙1t=x2t,x˙2t=−dx1t−cx2t+ix1t+qt,where(49)qt=qcosωt,ix1t=−lx13t−ax15t.The response system is(50)y˙1t=y2t+u1t,y˙2t=−dy1t−cy2t+iy1t+qt+u2t.The controller is(51)u1t=k1e1t+e2t,u2t=k2e1t+k3e2t.The error system is defined as(52)e1t=x1t−y1t,e2t=x2t−y2t,and then(53)e˙1t=−k1e1t,e˙2t=−k2+de1t−k3+ce2t+ix1t−iy1t.According to the method proposed in the present work, the synchronization criteria for (48) and (50) can be described as(54)Φ1=−k2+dq12−q11k1+λq12<0,Φ2=−q22k3+c<01−k2+k3+cq12−k2+dq22+λq222≤4Φ1Φ2.LetQ=1001, and then(55)k1>0,c+k3>0,2k1c+k2−d+k2>λ.The estimation method of system bound values is(56)xit≤ni,i=1,2,∀t>0,(57)y1t≤n1+x10−y10,∀t>0,k1>0(58)i˙γt=−3λγ2t+5aγ4t,and(59)i˙γt≤λ,where(60)λ=3l+5aαα,and(61)α=maxγ2t,∀γt∈minx1t,y1t,maxx1t,y1t.The merits of the synchronization criteria referred to in this work can be found. As expected, the nonlinear terms in error system (53) are retained, and the bounds of y1t,y2t can be easily estimated by (56)–(61). Thus, the stability criteria of the error system described by (54) required by the derivation of the stability is obtained.


### 4.2. Application of the Four-Axis Duffing Oscillatory System

Regarding the six-axis Duffing oscillatory system, ifa=0, then it is converted to a four-axis Duffing oscillatory system as follows:(62)x¨t=cx˙t−dxt−lx3t+q⋅cosωt.Letx1t=xt and x2t=x˙1t, and then(63)x˙1t=x2t,x˙2t=−dx1t−cx2t+ix1t+qt,where(64)qt=q⋅cosωt,gx1t=−lx13t.The response system is(65)y˙1t=y2t+u1t,y˙2t=−dy1t−cy2t+iy1t+qt+u2t.The controller is(66)u1t=k1e1t+e2t,u2t=k2e1t+k3e2t.The error is defined as(67)e1t=x1t−y1t,e2t=x2t−y2t.The error system is(68)e˙1t=−k1e1t,e˙2t=−k2+de1t−k3+ce2t+ix1t−iy1t.According to the method of this work, the synchronization criteria for systems (63) and (65) are(69)Φ1=−k2+dq12−q11k1+λ˜q12<0,Φ2=−q22k3+c<0−k1+k3+cq12−k2+dq22+λ˜q222≤4Φ1Φ2.LetQ=1001, and then(70)k1>0,c+k3>0,2k1c+k3−d+k2>λ˜.The estimation method for the system bound values is as follows:(71)xit≤ni,i=1,2,∀t>0,(72)y1t≤n1+x10−y10,∀t>0,k1>0,(73)i˙γt=−3lγ2t,(74)i˙γt≤λ˜,(75)λ˜=3l⋅α,and(76)α=maxγ2t,∀γt∈minx1t,y1t,maxx1t,y1t.Note 2.
Han et al. [25], Njah, and Vincent et al. [26] established the synchronization schemes for four-axis Duffing equations and studied the master-slave synchronization criteria under active control. The criteria are as follows:The controller is(77)u1t=−y1t+x1t+k⌣1e1t,u2t=dy1t+cy2t+ly13t−dx1t−cx2t−lx13t+k⌣2e2t, where k⌣1,k⌣2 are gains of feedback; and the error system is(78)e˙1t=−k⌣1e1t,e˙2t=−k⌣2e2t.
As discussed in Note2, the error system described by (78) is a linear system. Compared with the control methods of prior studies [25, 26], both linear and nonlinear terms are retained in the present control method, and −dx1t−cx2t−lx13t is fully utilized to derive the synchronization control criteria (69).


## 4.1. Application of the Six-Axis Duffing Oscillatory System

In the previous section, the synchronization criteria for the six-axis Duffing oscillatory system in prior work [4, 5] was compared with that for the parametric excitation roll system of a ship in this work, but the method proposed in this work has not been applied to a conventional six-axis Duffing oscillatory system, which is to be presented here.The mathematical model of the six-axis Duffing oscillatory system is as follows:(47)x¨t=−cx˙t−dxt−lx3t−ax5t+qcosωtt.Letx1t=xt,x2t=x˙1t, then equation (47) becomes(48)x˙1t=x2t,x˙2t=−dx1t−cx2t+ix1t+qt,where(49)qt=qcosωt,ix1t=−lx13t−ax15t.The response system is(50)y˙1t=y2t+u1t,y˙2t=−dy1t−cy2t+iy1t+qt+u2t.The controller is(51)u1t=k1e1t+e2t,u2t=k2e1t+k3e2t.The error system is defined as(52)e1t=x1t−y1t,e2t=x2t−y2t,and then(53)e˙1t=−k1e1t,e˙2t=−k2+de1t−k3+ce2t+ix1t−iy1t.According to the method proposed in the present work, the synchronization criteria for (48) and (50) can be described as(54)Φ1=−k2+dq12−q11k1+λq12<0,Φ2=−q22k3+c<01−k2+k3+cq12−k2+dq22+λq222≤4Φ1Φ2.LetQ=1001, and then(55)k1>0,c+k3>0,2k1c+k2−d+k2>λ.The estimation method of system bound values is(56)xit≤ni,i=1,2,∀t>0,(57)y1t≤n1+x10−y10,∀t>0,k1>0(58)i˙γt=−3λγ2t+5aγ4t,and(59)i˙γt≤λ,where(60)λ=3l+5aαα,and(61)α=maxγ2t,∀γt∈minx1t,y1t,maxx1t,y1t.The merits of the synchronization criteria referred to in this work can be found. As expected, the nonlinear terms in error system (53) are retained, and the bounds of y1t,y2t can be easily estimated by (56)–(61). Thus, the stability criteria of the error system described by (54) required by the derivation of the stability is obtained.


## 4.2. Application of the Four-Axis Duffing Oscillatory System

Regarding the six-axis Duffing oscillatory system, ifa=0, then it is converted to a four-axis Duffing oscillatory system as follows:(62)x¨t=cx˙t−dxt−lx3t+q⋅cosωt.Letx1t=xt and x2t=x˙1t, and then(63)x˙1t=x2t,x˙2t=−dx1t−cx2t+ix1t+qt,where(64)qt=q⋅cosωt,gx1t=−lx13t.The response system is(65)y˙1t=y2t+u1t,y˙2t=−dy1t−cy2t+iy1t+qt+u2t.The controller is(66)u1t=k1e1t+e2t,u2t=k2e1t+k3e2t.The error is defined as(67)e1t=x1t−y1t,e2t=x2t−y2t.The error system is(68)e˙1t=−k1e1t,e˙2t=−k2+de1t−k3+ce2t+ix1t−iy1t.According to the method of this work, the synchronization criteria for systems (63) and (65) are(69)Φ1=−k2+dq12−q11k1+λ˜q12<0,Φ2=−q22k3+c<0−k1+k3+cq12−k2+dq22+λ˜q222≤4Φ1Φ2.LetQ=1001, and then(70)k1>0,c+k3>0,2k1c+k3−d+k2>λ˜.The estimation method for the system bound values is as follows:(71)xit≤ni,i=1,2,∀t>0,(72)y1t≤n1+x10−y10,∀t>0,k1>0,(73)i˙γt=−3lγ2t,(74)i˙γt≤λ˜,(75)λ˜=3l⋅α,and(76)α=maxγ2t,∀γt∈minx1t,y1t,maxx1t,y1t.Note 2.
Han et al. [25], Njah, and Vincent et al. [26] established the synchronization schemes for four-axis Duffing equations and studied the master-slave synchronization criteria under active control. The criteria are as follows:The controller is(77)u1t=−y1t+x1t+k⌣1e1t,u2t=dy1t+cy2t+ly13t−dx1t−cx2t−lx13t+k⌣2e2t, where k⌣1,k⌣2 are gains of feedback; and the error system is(78)e˙1t=−k⌣1e1t,e˙2t=−k⌣2e2t.
As discussed in Note2, the error system described by (78) is a linear system. Compared with the control methods of prior studies [25, 26], both linear and nonlinear terms are retained in the present control method, and −dx1t−cx2t−lx13t is fully utilized to derive the synchronization control criteria (69).


## 5. Simulation Study

### 5.1. Synchronous Simulation of the Parametric Excitation Roll Chaotic Six-Axis Duffing System of a Ship with Parametric and Forced Excitation

Regarding the parametric excitation roll six-axis Duffing systems (6) and (10) of a ship with parametric and forced excitation, the parameters a=0.069,b=0.5367,c=1.25,d=0.9926,(79)e=0.0998,f=2.875,ω=2.236,g=0.16were selected and the initial values(80)x10=x10=0,x20=x20=−1,y10=y10=0.1,y20=y20=−1.1were assigned. The chaotic attractor could be obtained from Figure 3 with a bound of x1≤3.5,x2≤2.5, and thus n1=3.5,n2=2.5. By equation (25), y1t≤3.5+0−−0.1=3.6 was obtained. By equation (26), y2t≤2.5+−1−−1.1=2.6 was obtained. By equations (27), (28), and (29), i˙γ1t≤λ1=122.44 was obtained. By equations (30), (31), and (32), j˙γ2t≤λ2=10.88 was obtained. If k2=0,k1=k3 was assigned, then k1=k3=69 could be obtained based on equation (44).Figures4–6 present the simulation results. As seen from the error effects shown in Figure 4, it can be observed that when t approximated 0.1 s, errors e1 and e2 almost stabilized around the zero point, indicating that system (16) achieved global asymptotic stability. As shown in Figures 5 and 6, when t was close to 0.1 s, x1 and y1of drive system (6) and x2 and y2 of response system (10) reached synchronization, indicating that the method proposed in this work was verified.Figure 4
Synchronization error of systems (6) and (10) k2=0,k1=k3=69.Figure 5
x1 and y1synchronization time response of systems (6) and (10)k2=0,k1=k3=69.Figure 6
x2 and y2synchronization time response of systems (6) and (10) k2=0,k1=k3=69.


### 5.2. Synchronous Simulation of a Six-Axis Duffing Oscillatory Chaotic System

Regarding six-axis Duffing oscillatory chaotic systems (48) and (50), the parameters a=0.1,c=0.4,d=1.1,l=0.4,q=1.8,andω=2.1 were selected, and the initial values x10=x10=0,x20=x20=−1 and y10=y10=0.1,y20=y20=−1.5 were assigned. The chaotic attractor could be derived from Figure 7 with a bound defined by x1≤1.2, and thus n1=1.2. Via equation (57), y1t≤1.2+0−0.1=1.3 was obtained, and by equations (59), (60), and (61), i˙γt≤λ=3.45605 was obtained. If k2=0,k1=k3 was assigned, then k1=k3=2.09 could be obtained based on equation (55).Figure 7
Phase diagram of six-axis Duffing oscillatory chaotic system (48).Figures8–10 present the simulation results. As shown from the error effects in Figure 8, when t approximated 2 s, errors e1 and e2 almost stabilized around the zero point, indicating that error system (53) achieved global asymptotic stability. As shown in Figures 9 and 10, when t was close to 2 s, x1 and y1 of drive system (48) and x2 and y2 of response system (50) reached synchronization, indicating that the method proposed in this work was verified.Figure 8
Synchronization error of six-axis Duffing oscillatory chaotic systems (48) and (50) k2=0,k1=k3=2.09.Figure 9
x1 and y1synchronization time response of six-axis Duffing oscillatory chaotic systems (48) and (50) k2=0,k1=k3=2.09.Figure 10
x2 and y2synchronization time response of six-axis Duffing oscillatory chaotic systems (48) and (50) k2=0,k1=k3=2.09.


### 5.3. Synchronous Simulation of a Four-Axis Duffing Oscillatory Chaotic System

Regarding four-axis Duffing oscillatory chaotic systems (63) and (65), the parameters a=0.1,c=0.4,d=1.1,l=0.4,q=1.8,andω=2.1 were selected and the initial values x10=x10=0,x20=x20=−1 and y10=y10=0.1,y20=y20=1.5were assigned. The chaotic attractor could be obtained in Figure 11 with a bound defined by x1≤1.2, and thus n1=1.2. By equation (72), y1t≤1.2+0−0.1=1.3 was obtained, and by equations (74), (75), and (76), i˙γt≤λ=2.028 was obtained. If k2=0,k1=k3 was assigned, then k1=k3=1.38 could be obtained based on equation (70).Figure 11
Phase diagram of four-axis Duffing oscillatory chaotic system (63).Figures12–14 present the simulation results. As shown from the error effects in Figure 12, when t approximated 3 s, errors e1 and e2 almost stabilized around the zero point, indicating that error system (64) achieved global asymptotic stability. As shown in Figures 13 and 14, when t neared 3 s, x1 and y1 of drive system (63) and x2 and y2 of response system (65) reached synchronization, indicating that the method proposed in this work was verified.Figure 12
Synchronization error of four-axis Duffing oscillatory chaotic systems (63) and (65) k2=0,k1=k3=1.38.Figure 13
x1 and y1 synchronization time response of four-axis Duffing oscillatory chaotic systems (63) and (65) k2=0,k1=k3=1.38.Figure 14
x2 and y2 synchronization time response of four-axis Duffing oscillatory chaotic systems (63) and (65) k2=0,k1=k3=1.38.


## 5.1. Synchronous Simulation of the Parametric Excitation Roll Chaotic Six-Axis Duffing System of a Ship with Parametric and Forced Excitation

Regarding the parametric excitation roll six-axis Duffing systems (6) and (10) of a ship with parametric and forced excitation, the parameters a=0.069,b=0.5367,c=1.25,d=0.9926,(79)e=0.0998,f=2.875,ω=2.236,g=0.16were selected and the initial values(80)x10=x10=0,x20=x20=−1,y10=y10=0.1,y20=y20=−1.1were assigned. The chaotic attractor could be obtained from Figure 3 with a bound of x1≤3.5,x2≤2.5, and thus n1=3.5,n2=2.5. By equation (25), y1t≤3.5+0−−0.1=3.6 was obtained. By equation (26), y2t≤2.5+−1−−1.1=2.6 was obtained. By equations (27), (28), and (29), i˙γ1t≤λ1=122.44 was obtained. By equations (30), (31), and (32), j˙γ2t≤λ2=10.88 was obtained. If k2=0,k1=k3 was assigned, then k1=k3=69 could be obtained based on equation (44).Figures4–6 present the simulation results. As seen from the error effects shown in Figure 4, it can be observed that when t approximated 0.1 s, errors e1 and e2 almost stabilized around the zero point, indicating that system (16) achieved global asymptotic stability. As shown in Figures 5 and 6, when t was close to 0.1 s, x1 and y1of drive system (6) and x2 and y2 of response system (10) reached synchronization, indicating that the method proposed in this work was verified.Figure 4
Synchronization error of systems (6) and (10) k2=0,k1=k3=69.Figure 5
x1 and y1synchronization time response of systems (6) and (10)k2=0,k1=k3=69.Figure 6
x2 and y2synchronization time response of systems (6) and (10) k2=0,k1=k3=69.


## 5.2. Synchronous Simulation of a Six-Axis Duffing Oscillatory Chaotic System

Regarding six-axis Duffing oscillatory chaotic systems (48) and (50), the parameters a=0.1,c=0.4,d=1.1,l=0.4,q=1.8,andω=2.1 were selected, and the initial values x10=x10=0,x20=x20=−1 and y10=y10=0.1,y20=y20=−1.5 were assigned. The chaotic attractor could be derived from Figure 7 with a bound defined by x1≤1.2, and thus n1=1.2. Via equation (57), y1t≤1.2+0−0.1=1.3 was obtained, and by equations (59), (60), and (61), i˙γt≤λ=3.45605 was obtained. If k2=0,k1=k3 was assigned, then k1=k3=2.09 could be obtained based on equation (55).Figure 7
Phase diagram of six-axis Duffing oscillatory chaotic system (48).Figures8–10 present the simulation results. As shown from the error effects in Figure 8, when t approximated 2 s, errors e1 and e2 almost stabilized around the zero point, indicating that error system (53) achieved global asymptotic stability. As shown in Figures 9 and 10, when t was close to 2 s, x1 and y1 of drive system (48) and x2 and y2 of response system (50) reached synchronization, indicating that the method proposed in this work was verified.Figure 8
Synchronization error of six-axis Duffing oscillatory chaotic systems (48) and (50) k2=0,k1=k3=2.09.Figure 9
x1 and y1synchronization time response of six-axis Duffing oscillatory chaotic systems (48) and (50) k2=0,k1=k3=2.09.Figure 10
x2 and y2synchronization time response of six-axis Duffing oscillatory chaotic systems (48) and (50) k2=0,k1=k3=2.09.


## 5.3. Synchronous Simulation of a Four-Axis Duffing Oscillatory Chaotic System

Regarding four-axis Duffing oscillatory chaotic systems (63) and (65), the parameters a=0.1,c=0.4,d=1.1,l=0.4,q=1.8,andω=2.1 were selected and the initial values x10=x10=0,x20=x20=−1 and y10=y10=0.1,y20=y20=1.5were assigned. The chaotic attractor could be obtained in Figure 11 with a bound defined by x1≤1.2, and thus n1=1.2. By equation (72), y1t≤1.2+0−0.1=1.3 was obtained, and by equations (74), (75), and (76), i˙γt≤λ=2.028 was obtained. If k2=0,k1=k3 was assigned, then k1=k3=1.38 could be obtained based on equation (70).Figure 11
Phase diagram of four-axis Duffing oscillatory chaotic system (63).Figures12–14 present the simulation results. As shown from the error effects in Figure 12, when t approximated 3 s, errors e1 and e2 almost stabilized around the zero point, indicating that error system (64) achieved global asymptotic stability. As shown in Figures 13 and 14, when t neared 3 s, x1 and y1 of drive system (63) and x2 and y2 of response system (65) reached synchronization, indicating that the method proposed in this work was verified.Figure 12
Synchronization error of four-axis Duffing oscillatory chaotic systems (63) and (65) k2=0,k1=k3=1.38.Figure 13
x1 and y1 synchronization time response of four-axis Duffing oscillatory chaotic systems (63) and (65) k2=0,k1=k3=1.38.Figure 14
x2 and y2 synchronization time response of four-axis Duffing oscillatory chaotic systems (63) and (65) k2=0,k1=k3=1.38.


## 6. Conclusions

A synchronization study was conducted for a six-axis Duffing oscillatory chaotic system. A linear state error feedback method was employed, and the parametric excitation roll chaotic six-axis Duffing system of a ship with parametric and forced excitation was regarded as the main object of study. These were applied to typical six-axis Duffing oscillatory system and four-axis Duffing oscillatory system, and the validity of the synchronization criteria for Duffing oscillators was demonstrated by simulation verification. In the control process, not only are the nonlinear terms of original Duffing system reserved in the error system but the synchronization criteria are derived by the prediction of trajectory bounds of the response system. The synchronized drive system and response system in this work share the same structure, and the synchronization of chaos system of different structures can be regarded as a future research interest.



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*Source: 6681562-2021-01-27.xml*