3.2. Controlling Hyperchaotic System (6) with Known Parameters

If we make simple change into control (7) i.e. change only second equation to become the following forms:

(13)

and used the same of the Lyapunov function in Equation (10). Then, we get the time derivative for the Lyapunov function as the following:

(14)

where

By this control, we obvious the matrix is not diagonal and contains parameters. Here are two ways to tell if it’s above matrix is a positive definite, in the first way, we can use determinants test or pivot test without we know the value of these parameters while in the second way (based on corollary 1.), We must give the value of these parameters in order to translate the matrix to the diagonal matrix. Consequently, the control problem with unknown parameters is replaced by the equivalent problem of controlling with known parameters. But if we substitute the value of these parameters as, , and in the matrix we get a matrix with a negative definition and the method is failing, For treatment this problem we must choose a suitable Lyapunov function or modify the matrix P to make the matrix is a positive definite, and the following theorem explains this modification.

Theorem 2. The controlled hyperchaotic system (6) with nonlinear control (13) is globally asymptotically stable.

Proof. The system (6) with control (13) becomes:

(15)

This system can be reformulated in the following form:

(16)

Now, according to the Lyapunov second method, Let us modify the Lyapunov function by the following form i.e. modify the matrix p to get the matrix:

(17)

where is a diagonal matrix

So, we have the time derivative of the Lyapunov function as:

(18)

where is also diagonal matrix

Consequently, we translate the matrix (not diagonal) to matrix (diagonal) for the same control (13) after we input the value of parameters, at the same time we modified the matrix p to become the matrix. Then, we get, which gives asymptotic stability of the system (6) by Lyapunov stability theory. This means that the controller proposes is achieved the suppressed of system (6).

On the other hand, the control problem for a system (6) with control (13) can be achieved by linearization method. Then, we have the characteristic equation forms:

(19)

Since the parameters are known. So, we have

(20)

and the roots of the above equation are. Therefore, all roots with negative real parts. Consequently, we achieved the control system (6) by linearization method, the proof is complete.

Remark1. We founded the roots of Equation (20) by numerical methods. Also, we can use Gardan and Routh-Hurwitz methods for it.

Obviously, from theorem 1, the matrix with control (7) is succeed to achieve the control directly while in theorem 2, we needed to modify the matrix P to become in order to equivalent the control (13), but in some time, it is impossible to modify the matrix P to ensure the convergence of the zero by using Lyapunov stability theory which is refer to the weakness for this method and the following theorem explain this case.

Theorem 3. If the nonlinear controllers are proposed as:

(21)

i.e. simple change in four equation for control (7). Then, the zero solution of the controlled hyperchaotic system (6) can’t convergent by Lyapunov stability theory and is globally asymptotically stable by linearization method.

Proof. Substituting the controllers (21) in the system (6), we have

(22)

Also system (22) can be rewritten (According to the formulation (1)) as:

(23)

To check the control of this system by using Lyapunov stability theory, we can construct a Lyapunov function as

and then we have the time derivative as the following:

(24)

Here is not diagonal matrix and contains a parameter

So it is impossible to turn this matrix to a diagonal matrix with positive parameter, and we have is a positive definite function and failed this method to control (21). In order to overcome this problem, we used the linearization method. Then, we have the characteristic equation forms:

(25)

Since the parameters are known, we have

(26)

And the roots of this equation are therefore we succeed to achieve the chaos control for system (6) with control (21) by linearization method only.

4. Chaos Synchronization

In this section, we consider the synchronization problem of the hyperchaotic system (5) with known and unknown parameters using corollary 1. and how we can apply this corollary to determine between them,

Let us consider the hyperchaotic system (5) as the drive system, and the controlled hyperchaotic system (6) as the response system.

Subtracting system (5) from the system (6), we obtain the error dynamical system between the drive system and the response system which is given by:

(27)

where and.

System (27) describes the error dynamics according to formulation 4.

4.1. Chaos Synchronization of System (27) with Unknown Parameters

Theorem 4. The zero solution of the error dynamical system (27) is asymptotically stable if nonlinear control is designed as following:

(28)

Proof. Substituting the controllers (28) in the system (27), we have

(29)

According to the formulation (1), the above system can be rewritten as:

(30)

Based on the Lyapunov stability theory, we construct the following Lyapunov candidate function

(31)

And the time derivative of the Lyapunov function is:

(32)

Here. So, we perform synchronization with unknown parameters according to corollary 1. We have is a negative definite function. Hence, the system (27) can asymptotically converge to the unstable equilibrium with the controllers (28).

As well, by using the linearization method, we have the characteristic equation as:

(33)

By Routh-Hurwitz method, Equation (33) has all roots with negative real parts if and only if and. So, it is clear that satisfies the conditions for this method. Consequently, according to two methods (Lyapunov stability theory, linearization method) system (27) with control (28) is globally asymptotically stable, the proof is completed.

4.2. Chaos Synchronization of System (27) with Known Parameters

Based on the previously discussed in Section 3 to make simple change into a new control (change only in first equation for control 28) to become as:

(34)

and used the same of the Lyapunov function in Equation (31). Then, we take the time derivative for the Lyapunov function as the following:

(35)

where

Obviously, the matrix is not diagonal, and contains parameters. Therefore, we can achieve chaos control with known parameters according to corollary 1, and we must modify the matrix P in order to become the matrix is a positive definite, and the following theorem treated this case.

Theorem 5. The error dynamical system (27) with control (34) is globally asymptotically stable.

Proof. The system (27) with control (34) becomes as:

(36)

This system can be reformulated in the following form:

(37)

Now, according to the Lyapunov second method, Let us modify the Lyapunov function of the following form:

(38)

where is a diagonal matrix

So, we have the time derivative of the Lyapunov function as:

(39)

where is also diagonal matrix

We translate the matrix (not diagonal) to matrix (diagonal) for the same control (34) after we input the value of parameters at the same time we modified the matrix p to become the matrix. Then we get

which gives asymptotic stability of the system (27) by Lyapunov stability theory. This means that the controller proposes is achieved the suppressed of system (27).

On the other hand, the control problem for a system (27) with control (34) can be achieved by linearization method. Then, we have the characteristic equation forms:

(40)

Since the parameters are known, we have

(41)

And the roots of this equation are therefore we succeed to achieve the synchronization of the drive system (5) and the response system (6).

In adding, if we choose nonlinear control (simple change into four equations for control (34)) as:

(42)

with the matrix p then we have the matrix

To translate the matrix into diagonal matrix we must modify the matrix p in the following form then we have

(43)

where is also diagonal matrix.

Obviously, from theorem 4, we can perform the controlling of error dynamics system (27) by using controller (28) with matrix p directly, while in theorem 5 we must modify the matrix P to become in order to equivalent the control (34), but in some time, it is impossible to modify the matrix P to guarantee the convergent to the zero by using Lyapunov stability theory and the following theorem to explain this case.

Theorem 6. If the nonlinear controllers are proposed as:

(44)

We multiplied the parameter d by number 2 in equation four for control (28). Then, it is impossible to perform the synchronization by Lyapunov stability theory.

Proof. The system (27) with control (44) becomes as

(45)

This system can be reformulated in the following form:

(46)

Now, to check the control of this system by using Lyapunov stability theory, we can construct a Lyapunov function as and then we have the time derivative as the following:

(47)

Here, is not diagonal matrix and contains a parameter

So it is impossible to transient the matrix into a diagonal matrix with positive parameter, and we have is a positive definite function and failed this method to control (44). In order to overcome this weakness, we used the linearization method. Then, we have the characteristic equation forms:

(48)

Since the parameters are known, we have

(49)

and the roots of this equation are therefore we succeed to achieve the chaos control for system (6) with control (44) by linearization method only.

Verification, we can be used the numerical simulation to validate these proposed controls, we choose the parameters, , and and the initial values of the drive system and the response system are and (5,3,35,-10) respectively. From Figure 1(a), we can see the behavior of the system (5) without control, while Figures 1(b)-(d) represent the behavior of the system (6) with control (7), (13) and control (21) respectively. And in Figure 2(a), there is no synchronization between two identical hyperchaotic systems without control, while in Figures 2(b)-(d) we can see the synchronization between the drive

Figure 1. The attractor of space system (5) in x-y-w (a) Before control, (b) with controller (7), (c) with controller (13) (d) with controller (21).

(a) (b)(c) (d)

Figure 2. The synchronization between the drive system (5) and the response system (6) (a) without control, (b) with controller (28), (c) with controller (34), (d) with controller (44).

system (5) and the response system (6) with control (28), (34) and control (44) respectively.

5. Conclusions

In this paper, we study the chaos control for 4D hyperchaotic system based on Lyapunov stability theory, where this method is effective and accurate in finding stability of systems, and in view of dealing with nonlinear parts of systems and not neglecting those parts which support the strength and accuracy.

Nevertheless, it loses this property in some time. As the case of the system in this paper, we design the control to ensure the survival of nonlinear parts in the system. And in some cases, it can suppress without knowing the parameters of the system and the other cases. We must know that the parameters and third case can’t be suppressed. Then, the Lyapunov stability theory will be failed in sometimes. Therefore, we use the linear approximation method to ensure the validity of this proposed control. We have succeeded in achieving control, and we find that the simple difference in the control is responsible to get these three cases. Through this method, we can treat every case when the nonlinear parts have no effect on the system. Finally, numerical simulations show the effectiveness of the proposed chaos control and synchronization schemes.

Cite this paper

M. Aziz, M. and Al-Azzawi, S. (2016) Control and Synchronization with Known and Unknown Parameters.*Applied Mathematics*, **7**, 292-303. doi: 10.4236/am.2016.73026.

M. Aziz, M. and Al-Azzawi, S. (2016) Control and Synchronization with Known and Unknown Parameters.

References

[1] Al-Azzawi, S.F. (2012) Stability and Bifurcation of Pan Chaotic System by Using Routh-Hurwitz and Gardan Method. Applied Mathematics and Computation, 219, 1144-1152.

http://dx.doi.org/10.1016/j.amc.2012.07.022

[2] Aziz, M.M. and Al-Azzawi, S.F. (2015) Control and Synchronization of a Modified Hyperchaotic Pansystem via Active and Adaptive Control Techniques. Computational and Applied Mathematics Journal, 1, 151-155.

http://www.aascit.org/journal/cam

[3] Jia, Q. (2008) Hyperchaos Synchronization between Two Different Hyperchaotic Systems. Journal of Information and Computing Science, 3, 73-80.

[4] Lu, D., Wang, A. and Tian, X. (2008) Control and Synchronization of a New Hyperchaotic System within Known Parameters. International Journal of Nonlinear Science, 6, 224-229.

[5] Cai, G. and Tan, Z. (2007) Chaos Synchronize of a New Chaotic System via Nonlinear Control. Journal of Uncertain Systems, 1, 235-240.

http://www.jus.org.uk

[6] Ahmad, I., Saaban, A.B., Ibrahim, A.B. and Shahzad, M. (2015) A Research on Synchronization of Two Novel Chaotic System Based on Nonlinear Active Control Algorithm. Engineering, Technology & Applied Science Research, 5, 739-747.

[7] Aziz, M.M. and Al-Azzawi, S.F. (2015) The Relationship between Feedback Controller Numbers and Speed of Convergent in Control and Synchronization via Nonlinear Control. International Journal of Electrical and Electronic Science, 2, 374-380.

http://www.aascit.org/journal/ijees

[8] Chen, C., Fan, T. and Wang, B. (2014) Inverse Optimal Control of Hyperchaotic Finance System. World Journal of Modelling and Simulation, 10, 83-91.

[9] Zhu, C. (2010) Control and Synchronize a Novel Hyperchaotic System. Applied Mathematics and Computation, 216, 276-284.

http://dx.doi.org/10.1016/j.amc.2010.01.053

[10] Xu, J., Cai, G. and Zheng, S. (2009) Adaptive Synchronization for an Uncertain New Hyperchaos Lorenz System. International Journal of Nonlinear Science, 8, 117-123.

[11] Yassen, M.T. (2003) Adaptive Control and Synchronization of a Modified Chua’s Circuit System. Applied Mathematics and Computation, 135, 113-128.

http://www.elsevier.com/locate/amc

http://dx.doi.org/10.1016/S0096-3003(01)00318-6

[12] Yu, W. (2010) Stabilization of Three-Dimensional Chaotic Systems via Single State Feedback Controller. Physics Letters A, 374, 1488-1492.

http://dx.doi.org/10.1016/j.physleta.2010.01.048

[13] Chen, H.K. (2005) Global Chaos Synchronization of New Chaotic Systems via Nonlinear Control. Chaos, Solitons & Fractals, 23, 1245-1251.

http://dx.doi.org/10.1016/j.chaos.2004.06.040

[14] Park, J.H. (2005) Chaos Synchronization of a Chaotic System via Nonlinear Control. Chaos, Solitons & Fractals, 25, 579-584.

http://dx.doi.org/10.1016/j.chaos.2004.11.038

[15] Sundarapandian, V. (2012) Adaptive Control and Synchronization of the Cai System. International Journal on Cybernetics & Informatics, 1, 17-30.