The optimal control synchronization of complex dynamical networks with time-varying delay using PSO

被引：23
|
作者：
Chang, Qi [2 ]
Yang, Yongqing [1 ,2 ]
Sui, Xin [2 ]
Shi, Zhicheng [1 ]
机构：
[1] Jiangnan Univ, Wuxi Engn Res Ctr Biocomp, Sch Sci, Wuxi 214122, Peoples R China
[2] Jiangnan Univ, Sch IoT Engn, Wuxi 214122, Peoples R China
来源：
NEUROCOMPUTING | 2019年 / 333卷
关键词：
Exponential synchronization; Complex dynamical networks; Event-triggered control; Optimal control; PSO algorithm; LINEAR MULTIAGENT SYSTEMS; EVENT-TRIGGERED CONTROL; EXPONENTIAL SYNCHRONIZATION; NEURAL-NETWORKS; SWITCHING TOPOLOGIES; CONSENSUS; ARRAY; OPTIMIZATION; STRATEGY; DESIGN;
D O I：
10.1016/j.neucom.2018.12.020
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The paper mainly deals with the exponential synchronization of complex dynamical networks with a time-varying delay. Based on the event-triggered control, some sufficient conditions for exponential synchronization of complex dynamical networks can be achieved by using the Lyapunov function method, along with the Linear Matrix Inequality (LMI) and matrix analysis. The Zeno-behavior is excluded for the designed event-trigger controller. The optimal control parameters satisfying a minimal integral square error (ISE) index and control energy can be computed by using the particle swarm optimization (PSO) algorithm. A numerical example is given to demonstrate the effectiveness and applicability of the proposed approach. (C) 2018 Published by Elsevier B.V.
引用
收藏
页码：1 / 10
页数：10
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