Adaptive learning control synchronization for unknown time-varying complex dynamical networks with prescribed performance

This paper proposes a prescribed performance adaptive learning control scheme for complex dynamical networks. It can ensure that the states of all nodes in the complex dynamical networks can synchronize to the specified target trajectory, and satisfy prescribed performance constraints. Based on Lyapunov stability theory, it is proved that all signals in the closed-loop systems are bounded and the synchronization errors converge to a prescribed residual set. Simulation results are presented to show the validity of the proposed approach.

Automated summary

This paper proposes a prescribed performance adaptive learning control scheme for complex dynamical networks, which ensures that the states of all nodes synchronize to the specified target trajectory while satisfying performance constraints. Based on Lyapunov stability theory, it is proven that all signals in the closed-loop systems are bounded and the synchronization errors converge to a prescribed residual set. Simulation results validate the proposed approach.