A Study on Adaptive Synchronization Method for Network Systems

Abstract

This thesis proposes a robust adaptive controller design method for synchronization of network systems. We consider complex dynamical networks consisting of interconnected nodes, which have both uncertainty and disturbance. A fuzzy disturbance observer is used to estimate the uncertain factors without any prior knowledge about them. It is different from existing studies which need some information about the uncertain factors. The proposed control method globally asymptotically synchronizes all nodes in the network with a desired node using adaptation laws obtained by using Lyapunov stability theory. Two numerical examples are given to the effectiveness of our approach.Moreover, we investigate a synchronization problem for uncertain chaotic neural networks with the mixed time delays including time-varying and distributed delays. Uncertainty and disturbance in the network are estimated by fuzzy disturbance observer without any prior information about them, by applying update law for fuzzy parameter vector. The proposed control scheme with adaptive laws is derived based on Lyapunov-Krasovskii stability theory to guarantee the globally asymptotical synchronization between the networks. An example is illustrated to show the effectiveness of the proposed method.