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

被引：0
作者：
Aili Fan
Junmin Li
机构：
[1] Xidian University,Department of Applied Mathematics
来源：
Soft Computing | 2021年 / 25卷
关键词：
Complex dynamical networks; Synchronization errors; Prescribed performance constraints; Adaptive learning control;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：5093 / 5103
页数：10
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