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A new approach for trajectory tracking on uncertain complex networks is proposed. To achieve this goal, a neural controller is applied to a small fraction of nodes (pinned ones). Such 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 included. In order to verify the applicability and good performance of the proposed control scheme, a representative example is simulated, which consists of a complex network with each node described by a chaotic Lorenz oscillator. [ABSTRACT FROM AUTHOR]

In this technical note, a novel approach to trajectory tracking control of a complex network is presented, based on the idea and technique of inverse optimal pinning control. A control law that stabilizes the tracking error dynamics is developed. This controller allows different coupling strengths, and simultaneously minimizing an associated cost functional. Chaotic systems are considered to illustrate the applicability of the new methodology and the proposed controller effectiveness via simulations. [ABSTRACT FROM AUTHOR]