Synchronization of Complex Dynamical Networks with Nonidentical Nodes and Derivative Coupling via Distributed Adaptive Control

Adaptive synchronization control is proposed for a new complex dynamical network model with nonidentical nodes and nonderivative and derivative couplings. The distributed adaptive learning laws of periodically time-varying and constant parameters and distributed adaptive control are designed. The new method which can obtain the synchronization error of closed-loop complex network system is asymptotic convergence in the sense of square error norm. What is more, the coupling matrix is not assumed to be symmetric or irreducible. Finally, a simulation example shows the feasibility and effectiveness of the approach.