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Chaos, Solitons and Fractals 22 (2004) 927–933
www.elsevier.com/locate/chaos
Normal form and synchronization of strict-feedbackchaotic systems
Feng Wang a, Shihua Chen a,*, Minghai Yu b, Changping Wang c
a College of Mathematics and Statistics, Wuhan University, Wuhan 430072, PR Chinab State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, PR China
c Department of Mathematics and Statistics, Dalhousie University, Halifax, NS, Canada B3H 3J5
Accepted 9 March 2004
Abstract
This study concerns the normal form and synchronization of strict-feedback chaotic systems. We prove that, any
strict-feedback chaotic system can be rendered into a normal form with a invertible transform and then a design
procedure to synchronize the normal form of a non-autonomous strict-feedback chaotic system is presented. This
approach needs only a scalar driving signal to realize synchronization no matter how many dimensions the chaotic
system contains. Furthermore, the R€ossler chaotic system is taken as a concrete example to illustrate the procedure of
designing without transforming a strict-feedback chaotic system into its normal form. Numerical simulations are also
provided to show the effectiveness and feasibility of the developed methods.
� 2004 Elsevier Ltd. All rights reserved.
1. Introduction
The idea of synchronizing two identical chaotic systems with different initial conditions was first introduced by
Pecora and Carrol [1]. Since then, synchronization in coupled chaotic systems has become a rapidly developing field in
light of its great potential applications in secure communications, chemical reaction, modelling brain activity and so on
[2,3]. A large varieties of approaches have been proposed for chaos synchronization such as the adaptive synchroni-
zation method [4], the sampled-data feedback synchronization method [5], the impulsive synchronization method [6],
and many others [7–9]. Moreover, in the study of synchronizing nonlinear dynamics, several low-dimensional systems
are frequently used as benchmark examples for verification and validation of a proposed theory, method and algorithm.
These examples include Duffing oscillator, Van der Pol oscillator, R€ossler system, Chua’s circuit, Lorenz system and
Chen system [10–14].
It is very interesting to note that, many existing synchronization methods need several controllers to realize syn-
chronization and all those chaotic systems mentioned above can be rewritten into a class of nonlinear systems in a
so-called general strict-feedback form. This motivates the present work, which is to address two important issues
concerning the strict-feedback chaotic system. One issue is to find the normal form of strict-feedback chaotic systems,
the other is to find a scalar driving signal to realize synchronization. We prove that, any strict-feedback chaotic system
can be rendered into a normal form with a invertible transform. Furthermore, a design procedure to synchronize the
normal form of a non-autonomous strict-feedback chaotic system is presented. This approach needs only a scalar
driving signal to realize synchronization no matter how many dimensions the chaotic system contains. In addition, the
R€ossler chaotic system is taken as a concrete example to illustrate the procedure of designing without transforming a
* Corresponding author.
E-mail address: [email protected] (S. Chen).
0960-0779/$ - see front matter � 2004 Elsevier Ltd. All rights reserved.
doi:10.1016/j.chaos.2004.03.010
928 F. Wang et al. / Chaos, Solitons and Fractals 22 (2004) 927–933
strict-feedback chaotic system into its normal form. Numerical simulations are also provided to show the effectiveness
and feasibility of the developed methods.
2. The normal form of strict-feedback chaotic systems
Many chaotic systems used in the study of nonlinear dynamics can be rewritten into a class of nonlinear systems in
the form
_x1 ¼ f1ðx1; tÞx2 þ g1ðx1; tÞ;_x2 ¼ f2ðx1; x2; tÞx3 þ g2ðx1; x2; tÞ;. . . . . . . . .
_xn�1 ¼ fn�1ðx1; x2; . . . ; xn�1; tÞxn þ gn�1ðx1; x2; . . . ; xn�1; tÞ;_xn ¼ fnðx1; x2; . . . ; xn�1; xn; tÞ;
ð1Þ
where fi, gi ði ¼ 1; 2; . . . ; n� 1Þ are sufficiently smooth nonlinear functions and fn is a continuous nonlinear function. It
is often referred as a general strict-feedback system. If fi 6¼ 0 ði ¼ 1; 2; . . . ; n� 1Þ, it is called a strict-feedback system.
General strict-feedback systems and strict-feedback systems have been thoroughly studied during the past decades.
The main works concentrated on controlling these systems as well as tracking any one-dimensional smooth trajectory
[15,16]. Our aim in this section, however, is to find the normal form of strict-feedback chaotic systems. To this end, we
first define two new variables
u1 ¼ x1;
u2 ¼ f1ðx1; tÞx2 þ g1ðx1; tÞ, F1ðx1; x2; tÞ:ð2Þ
Simple calculation can yields
du2dt
¼ f1ðx1; tÞf2ðx1; x2; tÞx3 þX1
i¼0
o
oxiF1ðx1; x2; tÞ
dxidt
þ f1ðx1; tÞg2ðx1; x2; tÞ, F2ðx1; x2; x3; tÞ; ð3Þ
where x0 stands for t and dx0dt ¼ 1. With the above notation, the first two equations of (1) can be rewritten as
_u1 ¼ u2;
_u2 ¼ F2ðx1; x2; x3; tÞ:ð4Þ
We define the third variable as
u3 ¼ F2ðx1; x2; x3; tÞ: ð5Þ
One can get
du3dt
¼Y3i¼1
fiðx1; . . . ; xi; tÞx4 þX2
i¼0
o
oxiF2ðx1; x2; x3; tÞ
dxidt
þY2i¼1
fiðx1; . . . ; xi; tÞg3ðx1; x2; x3; tÞ, F3ðx1; x2; x3; x4; tÞ: ð6Þ
With this notation, the first three equations of (1) can be rewritten as
_u1 ¼ u2;
_u2 ¼ u3;
_u3 ¼ F3ðx1; x2; x3; x4; tÞ:ð7Þ
Generally, the k-th (36 i6 n� 1) variable is defined as
uk ¼ Fk�1ðx1; x2; . . . ; xk ; tÞ; ð8Þ
its time derivative is
dukdt
¼Yki¼1
fiðx1; . . . ; xi; tÞxkþ1 þXk�1
i¼0
o
oxiFk�1ðx1; . . . ; xk ; tÞ
dxidt
þYk�1
i¼1
fiðx1; . . . ; xi; tÞgkðx1; . . . ; xk ; tÞ, Fkðx1; . . . ; xkþ1; tÞ: ð9Þ
F. Wang et al. / Chaos, Solitons and Fractals 22 (2004) 927–933 929
Then the first k equations of (1) can be transformed into
_u1 ¼ u2;
_u2 ¼ u3;
. . . . . .
_uk�1 ¼ uk ;
_uk ¼ Fkðx1; . . . ; xkþ1; tÞ:
ð10Þ
The last step is to define the n-th variable as
un ¼ Fn�1ðx1; . . . ; xn; tÞ: ð11Þ
Its time derivative is
dundt
¼Yni¼1
fiðx1; . . . ; xi; tÞxn þXn�1
i¼0
o
oxiFn�1ðx1; . . . ; xn; tÞ
dxidt
: ð12Þ
On the other hand, from the definitions of uiði ¼ 1; 2; . . . ; nÞ one can find that xi ði ¼ 1; 2; . . . ; nÞ could be expressed
as functions of t and ui ði ¼ 1; 2; . . . ; nÞ. Thus, the equation (12) could be written in the form
dundt
¼ F ðu1; . . . ; un; tÞ; ð13Þ
from which we have the following theorem:
Theorem 2.1. Any strict-feedback chaotic system can be transformed into the normal form as follows:
_u1 ¼ u2;
_u2 ¼ u3;
. . . . . .
_un�1 ¼ un;
_un ¼ F ðu1; . . . ; un; tÞ:
ð14Þ
3. Synchronizing strict-feedback chaotic systems via a scalar driving signal
In this section, we will propose a design procedure to synchronize the normal form of the non-autonomous strict-
feedback chaotic system. We design such a scalar controller U that the controlled strict-feedback chaotic system in
normal form
_y1 ¼ y2;
_y2 ¼ y3;
. . . . . .
_yn�1 ¼ yn;
_yn ¼ F ðy1; . . . ; yn; tÞ þ U
ð15Þ
is synchronous with (14), the normal form of system (1). To this end, let
U ¼ F ðu1; . . . ; un; tÞ � F ðy1; . . . ; yn; tÞ þ U1; ð16Þ
where U1 is waiting for determination. With this selection, the error system of (15) and (14) can be expressed as
_e ¼ Aeþ BU ; ð17Þ
1930 F. Wang et al. / Chaos, Solitons and Fractals 22 (2004) 927–933
where e ¼ ðe1; e2; . . . ; enÞ ¼ ðy1 � u1; y2 � u2; . . . ; yn � unÞ>, B ¼ ð0; 0; . . . ; 1Þ> and
A ¼
0 1 0 0 . . . 00 0 1 0 . . . 0
. . . . . . . . . . . . . . . . . .0 0 0 0 . . . 1
0 0 0 0 . . . 0
0BBBB@
1CCCCA:
On the other hand, the matrix
½B AB A2B . . . An�1B � ¼
0 0 0 . . . 0 10 0 0 . . . 1 0
. . . . . . . . . . . . . . . . . .0 1 0 . . . 0 0
1 0 0 . . . 0 0
0BBBB@
1CCCCA
is full rank, so the linear control theory [17] confirms that the single-input dynamic system (17) is controllable, i.e., all
the eigenvalues are controllable. Thus, by the pole assigning method [17] we can select an appropriate feedback
gain vector k ¼ ðk1; k2; . . . ; knÞ> such that system (17) is globally asymptotically stable at zero with the state feed-
back control U1 ¼ ke, which implies that dynamical system (15) is synchronous with (14), the normal form of system
(1).
4. Numerical simulation
In order to demonstrate and verify the performance of the proposed method, numerical simulation with the R€osslerchaotic system is presented below. In what follows, the fourth-order Runge–Kutta method is applied to solve the system
of different equations with time step size equal to 0.001 in the numerical simulation.
Suppose the master system is the R€ossler chaotic system which is described by the following dynamical system [10]:
_x ¼ �y � z;
_y ¼ xþ ay;
_z ¼ bþ zðx� cÞ;ð18Þ
where a, b and c are system parameters. Obviously, it is not in the strict-feedback form. However, after a simple state
transformation, i.e. letting u1, u2, u3 replace y, x, z respectively, system (18) can be rendered into the following desired
strict-feedback form:
_u1 ¼ u2 þ au1;
_u2 ¼ �u3 � u1;
_u3 ¼ bþ u3ðu2 � cÞ:ð19Þ
The objective is to design such a scalar driving signal U that the controlled R€ossler chaotic system
_y1 ¼ y2 þ ay1;
_y2 ¼ �y3 � y1;
_y3 ¼ bþ y3ðy2 � cÞ þ U
ð20Þ
is synchronous with R€ossler chaotic system (19). We can transform the system into the normal form following the
procedure as above and then design the controller U by the pole assigning method. However, we can design a controller
directly without transforming R€ossler chaotic system into its normal form. First, let the error variables be e1 ¼ y1 � u1,e2 ¼ y2 � u2, e3 ¼ y3 � u3. Define the first variable
z1 ¼ y1 � u1 ¼ e1:
Its derivative along the solutions of system (19) and (20) is
_z1 ¼ ae1 þ e2:
F. Wang et al. / Chaos, Solitons and Fractals 22 (2004) 927–933 931
We select the first partial Lyapunov function as
V1 ¼1
2z21:
By simple calculation one can yield its time derivative along the solutions of system (19) and (20) as follows:
_V1 ¼ �z21 þ z1ððaþ 1Þe1 þ e2Þ: ð21Þ
Now, we define the second variable
z2 ¼ ðaþ 1Þe1 þ e2:
With this notation, Eq. (21) can be rewritten as
_V1 ¼ �z21 þ z1z2:
The second partial Lyapunov function is selected as
V2 ¼ V1 þ1
2z22 ¼
1
2z21 þ
1
2z22:
Its time derivative along the solutions of system (19) and (20) is
_V2 ¼ �z21 � z22 þ z2ððaþ 1Þ2e1 þ ðaþ 2Þe2 � e3Þ: ð22Þ
We define the third variable
z3 ¼ ðaþ 1Þ2e1 þ ðaþ 2Þe2 � e3:
Thus, Eq. (22) can be transformed into
_V2 ¼ �z21 � z22 þ z2z3: ð23Þ
We form the Lyapunov function as
V ðtÞ ¼ V2 þ1
2z23 ¼
1
2ðz21 þ z22 þ z23Þ: ð24Þ
Its time derivative along the solutions of systems (19) and (20) is
dV ðtÞdt
¼ �z21 � z22 � z23 þ z3ðz2 þ z3 þ ðaþ 1Þ2 _e1 þ ðaþ 2Þ _e2 � _e3Þ
¼ �z21 � z22 � z23 þ z3ðða3 þ 3a2 þ 3aÞe1 þ ða2 þ 3aþ 4Þe2 � ðaþ 3Þe3 � y3ðy2 � cÞ þ u3ðu2 � cÞ � UÞ: ð25Þ
Therefore, we have the following theorem:
Theorem 4.1. If we design the scalar driving signal U as
U ¼ ða3 þ 3a2 þ 3aÞe1 þ ða2 þ 3aþ 4Þe2 � ðaþ 3Þe3 � y3ðy2 � cÞ þ u3ðu2 � cÞ; ð26Þ
then the controlled R€ossler chaotic system (20) is globally asymptotically synchronous with R€ossler chaotic system (19).
Proof. Substituting (26) into (25), we have
dV ðtÞdt
¼ �z21 � z22 � z23 ¼ �2V ðtÞ;
from which we can yield V ðtÞ ¼ V ð0Þ expð�2tÞ. This implies limt!þ1 zi ¼ 0 ði ¼ 1; 2; 3Þ, i.e. limt!þ1 ei ¼ 0 ði ¼ 1; 2; 3Þ.Thus, the controlled R€ossler chaotic system (20) is globally asymptotically synchronous with R€ossler chaotic system
(19). h
In the numerical simulation, the system parameters a ¼ b ¼ 15, c ¼ 5:7, with which the R€ossler system behaves
chaotically. The initial conditions are set to be u1ð0Þ ¼ 3:0, u2ð0Þ ¼ 4:0, u3ð0Þ ¼ 5:0 and y1ð0Þ ¼ 6:0, y2ð0Þ ¼ 8:0,y3ð0Þ ¼ 9:0. Fig. 1 presents the results of the numerical simulation. One can see that the synchronization of all variables
is achieved successfully. Fig. 2 illustrates the control signal during the synchronization process.
Fig. 2. The control signal U during the chaos synchronization process.
Fig. 1. Graph of synchronization errors: e1ðtÞ ¼ y1 � u1; e2ðtÞ ¼ y2 � u2; e3ðtÞ ¼ y3 � u3:
932 F. Wang et al. / Chaos, Solitons and Fractals 22 (2004) 927–933
5. Conclusion
The strict-feedback chaotic system is studied. We prove that, any strict-feedback chaotic system can be rendered
into a normal form with a invertible transform. A design procedure to synchronize the normal form of the
strict-feedback chaotic system is presented. Furthermore, the R€ossler chaotic system is taken as a concrete example
to illustrate the procedure of designing without transforming the strict-feedback chaotic system into its nor-
mal form. Numerical simulations are also provided to show the effectiveness and feasibility of the developed
methods.
Acknowledgements
The work is supported by 973 Program of PR China (No. 2003CB415205) and the Opening Research Foundation
of the Key Laboratory of Water and Sediment Science (Wuhan University), Ministry of Education, P.R China
(No. 2003A001). The authors would like to thank the referees for their valuable comments.
F. Wang et al. / Chaos, Solitons and Fractals 22 (2004) 927–933 933
References
[1] Pecora LM, Carroll TL. Synchronization in chaotic system. Phys Rev Lett 1990;64(8):821–4.
[2] Chen G, Dong X. From chaos to order: methodologies, perspectives and applications. Singapore: World Scientific; 1998.
[3] Van�e�ecek A, �Celikovsk�y S. Control systems: From linear analysis to synthesis of chaos. London: Prentice-Hall; 1996.
[4] Chen S, L€u J. Synchronization of an uncertain unified chaotic system via adaptive control. Chaos, Solitons & Fractals
2002;14:643–7.
[5] Chen S, Liu J, Xie J, Lu J. Tracking control and synchronization of chaotic systems based upon sampled-data feedback. Chinese
Phys 2002;11(3):233–7.
[6] Chen S, Yang Q, Wang CP. Impulsive control and synchronization of unified chaotic system. Chaos, Solitons & Fractals
2004;20:751–8.
[7] Hendrik R. Controlling chaotic systems with multiple strange attractors. Phys Lett A 2002;300:182–8.
[8] Sun JT. Some global synchronization criteria for coupled delay-systems via unidirectional linear error feedback approach. Chaos,
Solitons & Fractals 2004;19:789–94.
[9] Kapitaniak T, Sekieta M, Ogorzalek M. Monotone synchronization of chaos. Int J Bifurcat Chaos 1996;6(1):211–7.
[10] R€ossler OE. An equation for continous chaos. Phys Lett A 1976;7:397–8.
[11] Lorenz EN. Deterministic non-periodic flows. J Atmos Sci 1963;20:130–41.
[12] Ueta T, Chen G. Bifurcation analysis of Chen’s attractor. Int J Bifurcation and Chaos 2000;10:1917–31.
[13] Yassen MT. Adaptive control and synchronization of a modified Chua’s circuit system. Applied Mathematics and Computation
2003;135:113–28.
[14] Miguel S, Carlos A, Ricardo B. On recovering the parameters and velocity state of the Duffing’s oscillator. Phys Lett A
2003;308:47–53.
[15] Wang C, Ge SS. Adaptive synchronization of uncertain chaotic systems via backstepping design. Chaos, Solitons & Fractals
2001;12:1199–206.
[16] Wang C, Ge SS. Adaptive backstepping control of a class of chaotic systems. Int J Bifurcation Chaos 2001;11:1115–8.
[17] Decarlo RA. Linear systems. Englewood Cliffs, NJ: Prentice Hall; 1989.