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.

中文翻译：

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具有规定性能的未知时变复杂动力学网络的自适应学习控制同步

本文提出了一种针对复杂动态网络的规定性能自适应学习控制方案。它可以确保复杂动态网络中所有节点的状态可以同步到指定的目标轨迹，并满足规定的性能约束。基于李雅普诺夫稳定性理论，证明了闭环系统中的所有信号都是有界的，并且同步误差收敛到规定的残差集。仿真结果表明了该方法的有效性。