Adaptive Synchronization for Complex Dynamical Networks with Uncertain Nonlinear Neutral-Type Coupling

Distributed adaptive synchronization control for complex dynamical networks with nonlinear derivative coupling is proposed. The distributed adaptive strategies are constituted by directed connections among nodes. By means of the parameters separation, the nonlinear functions can be transformed into the linearly form. Then effective distributed adaptive techniques are designed to eliminate the effect of time-varying parameters and made the considered network synchronize a given trajectory in the sense of square error norm. Furthermore, the coupling matrix is not assumed to be symmetric or irreducible. An example shows the applicability and feasibility of the approach.


Introduction
A complex network is a large set of interconnected nodes, where the nodes represent individuals in the graph and the edges represent the connections among them, such as climate system [1], biological neural networks [2], human brain system [3]. Many natural and man-made systems can be modeled and characterized by complex networks successfully [4]. Such systems may be characterized by a system with uncertainties, time delays, nonlinearity, neutral properties, hybrid dynamics, distributed dynamics and chaotic dynamics.
Synchronization phenomena has been found in different forms in complex networks, such as fireflies in the forest, description of hearts, and routing messages in the internet. Thus synchronization is one of meaningful issues in dynamical characteristics of the complex dynamical networks. A considerable number of papers on this topic have appeared (see [5][6][7] and references therein.) Recently, various control techniques have been reported to achieve networks synchronization (see [4,[8][9][10][11][12][13][14][15][16] and references therein) Some control schemes [8][9][10][11][12][13] were based on a solution of the homogenous system, in which it may be difficult to obtain the state information of an isolated node.
Consequently, utilizing the information from neighborhood to realize the network synchronization is more reasonable. Paper [17] introduced the concept of control topology to describe the whole controller structure. In [18], based on local information of node dynamics, an effective distributed adaptive strategy was designed to tune the coupling weights of a network. A considerable number of controlled synchronization techniques have been derived for complex dynamic networks based on the assumption which is the coupled nodes of CDN with the same dynamics (see the above papers and the references therein). In reality, complex networks are more likely to have different nodes for different dynamics. For example, in a multi-robot system, the robots can have distinct dynamic structures or different parameters. Recently, special attention has been focused on the synchronization of complex dynamical networks with nonidentical nodes [19][20][21][22]. Paper [20] investigated the synchronization problem of a complex network with nonidentical nodes via openloop controllers. The paper [22] considered nonlinearly coupled networks with non-identical nodes and designed pinning control to obtain synchronization criteria. On the other hand, many real-world network systems' structure will change over time and contain unknown parameters. Very recently, some papers studied complex networks with 2 Journal of Applied Mathematics unknown time-varying coupling strength [23][24][25][26]. In these results, non-identical nodes were not considered. Only in [21], the time-varying complex network with non-identical nodes was investigated, and a criterion of global bounded synchronization of the maximum state deviation between nodes was developed.
In other aspects, new complex networks models are proposed to reflect the complexity from the network structure. Thus, the problem of neutral-type couplings has also been widely investigated [27][28][29][30][31]. However, in the above studies, only linear derivative coupling is considered. More recently, [32] studied the synchronization in a class of dynamical networks with distributed delays and nonlinear derivative coupling. Considering the preceding discussion, nonidentical nodes complex dynamic network with nonlinear derivative coupling, and time-varying coupling strength is not concerned yet.
Inspired by the aforementioned results, the problem of adaptive synchronization is studied for complex dynamical networks with non-identical nodes, nonlinearly derivative couplings, and unknown time-varying coupling strength. A prominent feature of this network is that its complexity originates not only from the nonlinear dynamics of the nodes, but also from the complex coupling strength. The difficulty in dealing with the nonlinearly derivative couplings with unknown time-varying parameters is solved by using the parameter separation method. The distributed adaptive learning laws of periodically time-varying and constant parameters and the distributed adaptive controllers are constructed to guarantee that the system is asymptotically stable and that all closed loop signals are bounded.
The remainder of the paper is organized as follows. The problem statement and preliminaries are given in Section 2. Section 3 gives the main results and proofs. In Section 4, an illustrative example is provided to verify the theoretical results. Finally, conclusion is given.

Problem Statement and Preliminaries
The complex dynamical network is described aṡ

Remark 1.
It should be pointed out that the nonlinear derivative couplings consist of more information in CDN than the linear derivative couplings in [28], the challenge problem in (1) is due to the uncertain nonlinear neutral-type coupling ∑ =1 Γ (̇( ), ( )). For the unknown parameter ( ) and the nonlinear function (̇( ), ( )), in this paper, we shall overcome the obstacle though the domination technique and the parameters separation principle. Under later assumption, we can "separate" the parameters from the nonlinear function.
Define synchronization error as where ( ) ∈ is a solution of the dynamics of the isolated node to which all ( ) are expected to synchronize.
In order to derive the main results, the following assumptions and lemmas are introduced.
Remark 5. It is well known that the estimation of unknown nonlinear parameters in the systems is a difficult problem. In this paper, we separate the unknown parameters from the nonlinear function in (8), according to the separation principle in [33]; thus, Assumption 4 is reasonable and easily obtained. By using Assumption 4, we are able to solve the synchronization problem for a class of nonlinearly parameterized systems with nonlinear derivative couplings. Assumption 6. In the given networks in (1), ( ), ( ) are unknown time-varying periodic functions with a known period .
From Assumption 6, it is easy to see that ( ( )) and each element in ( ) are periodic functions with the same period . Suppose where ( ) is an unknown continuous periodic function with a known period and is an unknown constant parameter.
Assumption 8. Assume that the state and the state derivative of system (1) are measurable.

Remark 9.
This assumption is necessary to design controller and adaptive laws. Assumption 8 seems to be restrictive. The observer for state derivative will be considered in the near future.
The constant parameter distributed update law is designed as follows:̇( and the time-varying distributed periodic adaptive learning laws aŝ where The following theorem will give a sufficient condition for the controlled network in (5) to be asymptotical synchronization.
Consider the system (6) and the proposed control laws (14)- (16); it can be seen that the right-hand side of (6) is continuous with respect to all arguments. According to the existence theorem of differential equation, (6) has unique solution in the interval [0, 1 ) ⊂ [0, ) with 0 < 1 ≤ . This can guarantee the boundedness of ( ) over [0, 1 ). Therefore, we need only focus in the interval [ 1 , ).
The derivative of ( ) with respect to time is given bẏ Let us introduce some notations as From (6) and (13), the first term on the right hand side of (19) satisfies Journal of Applied Mathematics 5 According to Assumptions 2-7 and Lemma 10, from the above equation, we get where , are positive constants. Choosing = 1/2, = /4, and according to Lemma 11, we have Applying (14), the third term on the right-hand side of (19) satisfies Let us focus on the second and fourth terms on the righthand side of (19). In the interval [ 1 , ), since 0 ( ), 0 ( ) are continuous and strictly increasing functions, −1 ≤ −1 0 ( ) < ∞, −1 ≤ −1 0 ( ) < ∞ are ensured, we obtain Then from (26), the last term on the right-hand side of (19) satisfies Substituting (23)- (27) into (19) we obtaiṅ It is obvious that there exist sufficiently large positive constants such that According to (28) we havė For ∀ ∈ [ 1 , ), since ( ) is continuous and periodic and every element in matrix (⋅) is continuous function, the boundedness of them can be obtained. The boundedness of ( ) and tr( ) leads to the boundedness oḟ( ). That is, ( ) is bounded in [0, ). For ( 1 ) is bounded, the finiteness of ( ) is obvious by using integral technique, ∀ ∈ [0, ).
It is worth mentioning that when (⋅, ⋅) is bounded function, the boundedness oḟ( ) can be easy to get. Then, from Lemma 12 we can obtain the error globally asymptotical synchronization.
We choose( ) = 6 ( ( )), and the parameters are selected as follows: According to Theorem 14, the synchronization of the complex dynamical network can be guaranteed by the distributed adaptive controllers in (15) and the distributed adaptive learning laws when (16)- (18). Figure 1 shows the error evolutions under the designed controller. In this example, (⋅, ⋅) is bounded function, we clearly see that the states of the network asymptotically synchronizes with the states of the desired orbit. Figure 2 depicts the time evolution of the controller, and Figure 3 shows the evolution of the estimated time-varying parameters. Figures 2 and 3 show that all signals in the network are bounded. Figures 4 and 5 show that the time-varying parameters are periodic and bounded. Remark 15. It is not difficult to draw the evolution of other elements in parameter ( ). Here, we only take the first row of 11 ( ) for example. Compared with existing results [26,28], the biggest innovation of this paper is the asymptotical synchronization ability for the nonlinear neutral-type coupling complex networks under the designed controller.

Conclusion
In this paper, the synchronization problem for a complex dynamical network with nonlinearly derivative couplings is solved via distributed adaptive control method. The adaptive strategies are concerned with the networks topology. By combining inequality techniques and the parameter separation, introducing the composite energy function, the convergence of the tracking error and the boundedness of the system signals are derived. Moreover, the coupling matrix is not assumed to be symmetric or irreducible. Finally, a typical example was simulated to verify the proposed theoretical results.