ABSTRACT
In this chapter, a new approach for trajectory tracking of uncertain complex networks with identical and non-identical nodes is introduced. To achieve this goal, a neural controller is applied to a small fraction of nodes (pinned ones). Such a controller is composed of an on-line identifier based on a recurrent high-order neural network, and an inverse optimal controller to track the desired trajectory. A complete stability analysis is also presented. In order to verify the applicability and good performance of the control scheme, representative examples are simulated, which consist of a complex network with each node described by a chaotic Lorenz oscillator, and a network with different chaotic nodes.
