Application adaptive exponential synchronization of chaotic dynamical systems in secure communications
 Mahmoud Maheri^{1}Email author and
 Norihan Md Arifin^{2}
https://doi.org/10.1186/s1366201711586
© The Author(s) 2017
Received: 21 October 2016
Accepted: 21 March 2017
Published: 29 March 2017
Abstract
Synchronization of chaotic systems which is defined based on the exponential stability for encrypts of signals is presented in this paper. An adaptive control scheme is proposed, and the convergence of the synchronized error is guaranteed. Masking and modulation methods are applied for encryption. To verify the effectiveness of the proposed schemes, numerical simulation was applied on a wellknown system without linear term chaotic using MatLab software. The comparison of the proposed chaotic synchronization in the case of the synchronization rate and in the decryption precision of sent signal and image are shown.
Keywords
1 Introduction
Chaotic systems are nonlinearly deterministic and sensitive to initial conditions, they have been studied since the two most recent decades [1–4]. Chaotic systems have a lot of applications in different sciences such as laser, secure communication, biology sciences, nonlinear circuits, neural and computer networks [5–10].
The control of chaos is one of the main issues in the study of chaotic systems. The synchronization is one of the main control methods of these systems, which were introduced in a paper by Pecorra and Carrol in 1990 [3]. In recent years, synchronization of chaotic systems has become very attractive and has been applied in vast area of engineering, physic, computer networks, and so on which aroused increasing interest as regards use in new areas such as cryptography [11–15].
Up to now, many synchronization methods such as adaptive control, nonlinear feedback control and sliding mode control were proposed and applied successfully on chaotic systems [16–21]. But almost all of them used Lyapunov stability theorems to guarantee asymptotically synchronization and convergence is slow to the origin.
The exponential synchronization is another proposed method in this field, which has certain precision and stability in error systems. This method is robust and faster than the Lyapunov stability one. Tong et al. [22] applied exponential synchronization for stochastic neural networks with multidelayed and Markovian switching via adaptive feedback control, Yan and Yu [23], Lio and Yu [18] studied exponential synchronization for some wellknown classical dynamical systems. Yang used exponential stability in synchronization of a higher order chaotic system [17] etc.
So far, to the best of the authors’ knowledge, the problem of adaptive exponential synchronization and its applications for chaotic dynamical systems have received very little research attention.
In the recent work, the authors have proposed the adaptive based on exponential stability methods to secure chaos communication through the method of chaos masking and modulation. The general idea in the utilization of chaotic systems in using a transition of the desired signal is integration of it with a chaotic system and production of a chaotic signal. This signal is recoverable after synchronization. There are two major issues in this case; one includes how to produce the chaotic signal; this is usually conducted by both masking and modulation methods [24, 25].
The other most important issue is the time of transmittance and reception of the signal. The signal is produced and transmitted after synchronization of both slave and master systems. Thus, as the synchronization rate is high, the signal, with regard to its dependency on synchronization error, could be transmitted faster with better approximation.
This paper was conducted by introducing theorems related to the exponential stability and comparison of results in the encryption of signals based on Lyapunov stability and exponential stability.
The main contributions of this paper can be highlighted as follows. (1) A new adaptive synchronization method is studied for a fully nonlinear chaotic dynamical system based on the exponential stability theorem. (2) Application of the adaptive exponential synchronization obtained in encryption using modulation and masking methods.
The paper was organized as follows. Section 2, the definitions and theorems related to exponential and Lyapunov stability were provided. In Section 3, synchronization of a system without linear term is provided. Transmission and reception of the signal text by the modulation method was given for a wellknown system using exponential and Lyapunov stability in Section 4. Then the masking method and adaptive exponential stability were utilized to transmit a pictorial signal. Concluding remarks were provided in Section 5.
2 Preliminaries
2.1 Stability of autonomous systems
Definition 2.1

It is stable if, for any ϵ, there exists the value of \(\delta=\delta(\epsilon)\) so that$$\big\ x(0)\big\ < \delta\quad\Rightarrow \quad\big\ x(t)\big\ < \epsilon, \quad \forall t>0. $$

It is unstable if it is not stable.

It is asymptotically stable, if it is stable and there exist δ so that$$\big\ x(0)\big\ < \delta\quad\Rightarrow \quad\lim_{t\rightarrow\infty}x(t)=0. $$
For studying stability, a practical way is finding a Lyapunov function in linear and nonlinear systems without obtaining the response of them.
Theorem 2.1
Lyapunov stability theorem

\(\frac{\partial V}{\partial x_{i}}\), \(i=1,2,\ldots,n\), exists and is continuous;

V is positive definite,

If V̇ is semi negative definite, then the origin is a stable fixed point of the system.

If V̇ is negative definite, then the origin is a fixed point and asymptotically stable.

If V̇ is positive definite, then the origin is an unstable fixed point.
2.2 Stability of nonautonomous systems
The concepts of stability and asymptotically stability of a fixed point in nonautonomous systems correspond to Definition 2.1 in autonomous systems unless there is a new case, in that the response of autonomous system only depends on \((tt_{0})\), and the response of the nonautonomous system also depends on both t and \(t_{0}\). Thus, in the general state, the stability of fixed point depends on \(t_{0}\).
Definition 2.2

It is stable if, for any \(\epsilon>0\), the value of \(\delta =\delta(\epsilon,t_{0})\) is such that$$ \big\ x(t_{0})\big\ < \delta\quad\Rightarrow\quad\big\ x(t)\big\ < \epsilon,\quad \forall t\geq t_{0}\geq0. $$(3)

It is uniformly stable if, for any \(\epsilon>0\), the value of \(\delta=\delta(\epsilon)\) (independent of \(t_{0}\)) is such that equation (3) holds.

It is unstable, if is not stable.

It is asymptotically stable if is stable and there is a value such as \(c=c(t_{0})>0\) so that, for all values of \(\x(t_{0})\< c\), we have \(\lim_{t\rightarrow\infty}x(t)=0\).

It is uniform asymptotically stable if it is uniformly stable and there is a value such as \(c>0\) (independent of \(t_{0}\)) so that, for all values of \(\x(t_{0})\< c\), as \(t_{0}\) tends to infinity, \(x(t)\) monotonically tends to zero with respect to \(t_{0}\), in other words, for any \(\epsilon>0\), there exists a value such as \(t=T(\epsilon)\) so that$$\big\ x(t)\big\ < \epsilon, \quad\forall t\geq t_{0}+T(\epsilon), \forall\big\ x(t_{0})\big\ < c. $$

It is globally uniform asymptotically stable, if it is uniformly stable and, for any pair of positive numbers ϵ and c, there is a value \(t=T(\epsilon,c)>0\) so that$$\big\ x(t)\big\ < \epsilon, \quad \forall t\geq t_{0}+T(\epsilon,c), \forall\big\ x(t_{0})\big\ < c. $$
The uniform stability and uniform asymptotical stability could also be indicated based on specific numerical functions, i.e. class κ and class κι functions [28].
Definition 2.3
The continuous function \(\alpha: [0, a)\rightarrow[0, \infty)\) belongs to class κ if it strictly is increasing and \(\alpha (0)=0\). This function belongs to \(\kappa_{\infty}\) if \(a=\infty\) and with r increasing to infinity, \(a(r)\) also tends to infinity.
Definition 2.4
The continuous function \(\beta: [0, a)\times[0, \infty)\rightarrow [0, \infty)\) belongs to class κι if for any specific value s, the mapping \(\beta(r,s)\) with respect to r belongs to class κ and also for any specific value r, the mapping \(\beta(r,s)\) is decreasing with respect to s, and by increasing s to infinity, \(\beta(r,s)\) tends to zero.
Example 2.1
The function \(\alpha(r)=\arctan r\) is increasing strictly because \(\dot {\alpha}(r)=1/(1+r^{2})>0\). This function belongs to class κ, but it does not belong to class \(\kappa_{\infty}\), because \(\lim_{r\rightarrow \infty}\alpha(r)=\pi/2<\infty\).
The following lemma indicates some of explicit features of functions, class κ and κι.
Lemma 2.2

\(\alpha^{1}_{1}(\cdot)\) is defined in the interval \([0, \alpha_{1}(a)]\) belongs to class κ.

\(\alpha^{1}_{3}(\cdot)\) is defined in the interval \([0, \infty)\) and belongs to class \(\kappa_{\infty}\).

\(\alpha_{1}o\alpha_{2}\) belongs to class \(\kappa_{\infty}\).

\(\alpha_{3}o\alpha_{4}\) belongs to class \(\kappa_{\infty}\).

\(\sigma(r, s)=\alpha_{1}(\beta(\alpha_{2}(r), s))\) belongs to class \(\kappa_{\infty}\).
The following lemma explains the equivalence definitions for uniform stability and uniformly asymptotically stability based on class κ and κι functions.
Lemma 2.3
[30]

It is uniformly stable, if and only if function \(\alpha(\cdot)\) belongs to class κ and there is a positive constant c (independent of \(t_{0}\)) so that$$ \big\ x(t)\big\ \leq\alpha\bigl(\big\ x(t_{0})\big\ \bigr), \quad \forall t\geq t_{0}\geq0, \forall \big\ x(t_{0})\big\ < c. $$(4)

It is uniformly asymptotically stable, if and only if function \(\beta(\cdot, \cdot)\) belongs to class κι and there is a positive constant c (independent of \(t_{0}\)) so that$$ \big\ x(t)\big\ \leq\beta\bigl(\big\ x(t_{0})\big\ \bigr), \quad \forall t \geq t_{0}\geq0, \forall\big\ x(t_{0})\big\ < c. $$(5)

It is globally uniform asymptotically stable if and only if inequality (5) holds for any initial state of \(x(t_{0})\).
2.3 Exponential stability
As one of Lemma 2.3 results, it could be considered that in the case of nonautonomous systems, the stability and asymptotical stability based on definition (1) mean that there are functions of class κ and κι so that inequality (4) and (5) hold. Because in the nonautonomous systems, the stability and asymptotically stability of origin are uniform with respect to the initial time \(t_{0}\).
One particular case of uniform asymptotically stability occurs when, in equation (5), the function β belongs to class κι as \(\beta(r, s)=k r e^{\gamma s}\). This case is very important and could be considered one of distinctive features of fixed point stability.
Definition 2.5
Now, we explain the main theorem.
Theorem 2.4
If the class κ functions are of the particular form \(\alpha _{i}(r)=k_{i}r^{c}\), its application is easy in the proof of Theorem 2.4. In this case, it could be shown that the origin is exponentially stable.
Theorem 2.5
Proof
3 Synchronization based on the exponential stability
In this section, the synchronization system without linear term is introduced based on exponential and Lyapunov stability using nonlinear control functions.
Definition 3.1
Lemma 3.1
Proof
From Definition 3.1, \(\alpha=\frac {k_{2}}{k_{1}}E^{T}(t_{0})E(t_{0})\). Therefore, in this case, system (13) is exponentially stable and the synchronization of the drive system (12) and the response system (11) sufficiently achieved. □
Lemma 3.2
Theorem 3.3
Proof
The main benefit of the exponential synchronization method is the fast convergence. But there is a disadvantage in that, in practice, for some systems it is impossible or very difficult to obtain the exponential synchronization conditions. In this case, the exponential synchronization will change to synchronization based on the Lyapunov stability theorem. In simple terms, we can say exponential stability is Lyapunov stability, but the reverse is not true.
4 Application of chaos to secure communication
Encryption of information is carried out when two persons want to communicate with each other by an insecure communicative channel that can be a telephone or network but the third person could not identify the exchanged information by listening to them. What the first person wants to send to the second person is called the context text; it can be an image or a text by any language that is completely optional. Before sending the message, the transmitter of information should encrypt the information by a specific key. In this case, the context text is converted into the encrypted text. The encrypted text passes through a communicative channel of two individuals and the second person could decrypt the text by using the key and the text is converted into a context text.
Our goal is to use chaotic systems for encryption. Since aperiodic waves of the chaotic systems cannot be predicted, these systems are important in secure communication. Also chaotic systems are sensitive to initial conditions, which is another advantage for using these systems in different applications of secure communication. Here encryption using modulation and the masking method is studied.
4.1 Text modulation via adaptive exponential stability
In chaos modulation the information signal is injected into the transmitter. First we introduce some definitions, then we propose a fast mechanism for synchronization the chaotic systems without linear term (11) based on the driveresponse approach. Using exponential synchronization, the secure communication scheme is studied.
Definition 4.1
[33]
Definition 4.2
[33]
According to Definitions 4.1 and 4.2 the system (11) is observable algebraically with respect to the two outputs \(y_{1}\) and \(z_{1}\). The parameter b in equations (33) and (11) is identifiable algebraically with respect to the two other available outputs. Therefore, the nonavailable state variable \(x_{1}\) and the parameter b can be recovered simultaneously from the two other available outputs.
Theorem 4.1
Proof
4.2 Modulationbased adaptive Lyapunov stability
Theorem 4.2
Proof
To demonstrate and verify the validity of the modulation schemes, we confirmed our analytical studies be numerical simulations via MatLab software. We solve the dynamical systems using the fourth order RungeKutta numerical method. At the transmitter system side, the system parameters are chosen as \(a=0.1\), \(b=0.25\) while the arbitrary initial conditions are set as \(x_{1}(0)=1\), \(y_{1}(0)=1\), \(z_{1}(0)=3\). At the receiver system side, we fix the arbitrary initial conditions as \(x_{2}(0)=1\), \(y_{2}(0)=2\), \(z_{2}(0)=1\), \(a=0.1\), \(\hat{b}(0)=1\) and \(\hat{o}(0)=0.8\). Suppose the message signal \(o(t)\) is trigonometric signal such as \(o(t)=0.05\sin(60\pi t)\), which is added on the second state variable of the drive system.
By compression of results, it is obvious that in exponential synchronization the error system very fast tends to zero, which results in sending and receiving the signal faster with a better accuracy.
4.3 Image encryption using exponential stability
Almost in all cases the image encryption is often conducted along with data compression before be saved or transmitted. This is because of high volume of pictorial data and their adjuncts. Thus, it is so important to combine security demands with compression systems. In view of security, the pictorial data is not sensitive the same as the context data. In block method of encryption, firstly, one encryption key with equal dimensions with designed images one is produced by using of chaotic system and then the primary image encoded by block cipher.
Here, we have only one block. The given method could be exercised by producing keys with small sizes in a higher block number.
In order to decode, the encrypted image is produced by applying the same chaotic system and utilization of an equal initial value and by considering inversion methods in the decoding key.
5 Conclusion
In this paper, we showed that exponential stability method for the synchronization of chaotic dynamical systems is faster than of the Lyapunov stability theorem which it confirmed by the results obtained for a system without linear term. By using presented synchronization, cryptography of text and image were given through both modulation and masking methods in both active and adaptive cases. The results confirm the rate and precision of reception and transmittance of the desired signal.
Further research topics include the applying optimal control in the exponential synchronization of classical and fractional order chaotic systems and study of the other encryption methods such as the RivestShamirAdlemen (RSA) and Data Encryption Standard (DES) to compare them with the chaosbased encryption.
Declarations
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Authors’ Affiliations
References
 Lorenz, EN: Deterministic nonperiodic flow. J. Atmos. Sci. 20(2), 130141 (1963) View ArticleGoogle Scholar
 Ott, E, Grebogi, C, Yorke, JA: Controlling chaos. Phys. Rev. Lett. 64(11), 1196 (1990) MathSciNetView ArticleMATHGoogle Scholar
 Pecora, LM, Carroll, TL: Synchronization in chaotic systems. Phys. Rev. Lett. 64(8), 821 (1990) MathSciNetView ArticleMATHGoogle Scholar
 Yu, S, Lu, J, Leung, H, Chen, G: Design and implementation of nscroll chaotic attractors from a general jerk circuit. IEEE Trans. Circuits Syst. I, Regul. Pap. 52(7), 14591476 (2005) MathSciNetView ArticleGoogle Scholar
 Yu, F, Wang, C: Secure communication based on a fourwing chaotic system subject to disturbance inputs. Optik 125(20), 59205925 (2014) View ArticleGoogle Scholar
 Grassi, G, Mascolo, S: Synchronizing high dimensional chaotic systems via eigenvalue placement with application to cellular neural networks. Int. J. Bifurc. Chaos 9(4), 705711 (1999) View ArticleMATHGoogle Scholar
 Hsieh, JY, Hwang, CC, Wang, AP, Li, WJ: Controlling hyperchaos of the Rossler system. Int. J. Control 72(10), 882886 (1999) View ArticleMATHGoogle Scholar
 Kapitaniak, T, Chua, LO, Zhong, GQ: Experimental hyperchaos in coupled Chua’s circuits. IEEE Trans. Circuits Syst. I, Fundam. Theory Appl. 41(7), 499503 (1994) View ArticleGoogle Scholar
 Peng, JH, Ding, EJ, Ding, M, Yang, W: Synchronizing hyperchaos with a scalar transmitted signal. Phys. Rev. Lett. 76(6), 904 (1996) View ArticleGoogle Scholar
 Vicente, R, Daudén, J, Colet, P, Toral, R: Analysis and characterization of the hyperchaos generated by a semiconductor laser subject to a delayed feedback loop. IEEE J. Quantum Electron. 41(4), 541548 (2005) View ArticleGoogle Scholar
 PosadasCastillo, C, LópezGutiérrez, RM, CruzHernández, C: Synchronization of chaotic solidstate Nd:YAG lasers: application to secure communication. Commun. Nonlinear Sci. Numer. Simul. 13(8), 16551667 (2008) View ArticleGoogle Scholar
 Park, JH: Chaos synchronization of a chaotic system via nonlinear control. Chaos Solitons Fractals 25(3), 579584 (2005) View ArticleMATHGoogle Scholar
 Agiza, HN, Yassen, MT: Synchronization of Rossler and Chen chaotic dynamical systems using active control. Phys. Lett. A 278(4), 191197 (2001) MathSciNetView ArticleMATHGoogle Scholar
 Aghababa, MP: Robust stabilization and synchronization of a class of fractionalorder chaotic systems via a novel fractional sliding mode controller. Commun. Nonlinear Sci. Numer. Simul. 17(6), 26702681 (2012) MathSciNetView ArticleMATHGoogle Scholar
 Aghababa, MP, Aghababa, HP: Synchronization of nonlinear chaotic electromechanical gyrostat systems with uncertainties. Nonlinear Dyn. 67(4), 26892701 (2012) MathSciNetView ArticleMATHGoogle Scholar
 Mossa AlSawalha, M, Salmi Md Noorani, M: Antisynchronization of two hyperchaotic systems via nonlinear control. Commun. Nonlinear Sci. Numer. Simul. 14(8), 34023411 (2009) MathSciNetView ArticleMATHGoogle Scholar
 Yang, CC: One input control of exponential synchronization for a fourdimensional chaotic system. Appl. Math. Comput. 219(10), 51525161 (2013) MathSciNetMATHGoogle Scholar
 Liao, X, Yu, P: Study of globally exponential synchronization for the family of Rössler systems. Int. J. Bifurc. Chaos 16(8), 23952406 (2006) View ArticleMATHGoogle Scholar
 Wang, Z: Antisynchronization in two nonidentical hyperchaotic systems with known or unknown parameters. Commun. Nonlinear Sci. Numer. Simul. 14(5), 23662372 (2009) View ArticleGoogle Scholar
 Tong, D, Zhu, Q, Zhou, W, Xu, Y, Fang, J: Adaptive synchronization for stochastic TS fuzzy neural networks with timedelay and Markovian jumping parameters. Neurocomputing 117, 9197 (2013) View ArticleGoogle Scholar
 Tong, D, Zhang, L, Zhou, W, Zhou, J, Xu, Y: Asymptotical synchronization for delayed stochastic neural networks with uncertainty via adaptive control. Int. J. Control. Autom. Syst. 14(3), 706712 (2016) View ArticleGoogle Scholar
 Tong, D, Zhou, W, Zhou, X, Yang, J, Zhang, L, Xu, Y: Exponential synchronization for stochastic neural networks with multidelayed and Markovian switching via adaptive feedback control. Commun. Nonlinear Sci. Numer. Simul. 29(1), 359371 (2015) MathSciNetView ArticleGoogle Scholar
 Yan, Z, Yu, P: Globally exponential hyperchaos (lag) synchronization in a family of modified hyperchaotic Rössler systems. Int. J. Bifurc. Chaos 17(5), 17591774 (2007) View ArticleMATHGoogle Scholar
 Cuomo, KM, Oppenheim, AV, Strogatz, SH: Synchronization of Lorenzbased chaotic circuits with applications to communications. IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process. 40(10), 626633 (1993) View ArticleGoogle Scholar
 Boutayeb, M, Darouach, M, Rafaralahy, H: Generalized statespace observers for chaotic synchronization and secure communication. IEEE Trans. Circuits Syst. I, Fundam. Theory Appl. 49(3), 345349 (2002) MathSciNetView ArticleGoogle Scholar
 Stevens, BL, Lewis, FL, Johnson, EN: Aircraft Control and Simulation: Dynamics, Controls Design, and Autonomous Systems. Wiley, New York (2015) View ArticleGoogle Scholar
 Teschl, G: Ordinary Differential Equations and Dynamical Systems. Graduate Studies in Mathematics, vol. 140. Am. Math. Soc., Providence (2012) MATHGoogle Scholar
 Narendra, KS, Annaswamy, AM: Stable Adaptive Systems. Dover, Mineola (2012) MATHGoogle Scholar
 Khalil, HK: Nonlinear Systems, 3rd edn. Pearson, Harlow (2002) MATHGoogle Scholar
 Shankar, S, Bodson, M: Adaptive Control: Stability, Convergence and Robustness. Dover, Mineola (2011) MATHGoogle Scholar
 Aghababa, MP, Khanmohammadi, S, Alizadeh, G: Finitetime synchronization of two different chaotic systems with unknown parameters via sliding mode technique. Appl. Math. Model. 35(6), 30803091 (2011) MathSciNetView ArticleMATHGoogle Scholar
 Xu, Y, Wang, Y: A new chaotic system without linear term and its impulsive synchronization. Optik 125(11), 25262530 (2014) View ArticleGoogle Scholar
 MataMachuca, JL, MartínezGuerra, R, AguilarLópez, R, AguilarIbañez, C: A chaotic system in synchronization and secure communications. Commun. Nonlinear Sci. Numer. Simul. 17(4), 17061713 (2012) MathSciNetView ArticleGoogle Scholar