Neural-network-based average tracking for nonlinear multi-agent systems with switching topologies

被引：0

作者：

Zhang, Shangjun ^{[1
]}

Lyu, Jianting ^{[1
]}

Gao, Dai ^{[2
]}

Wang, Xin ^{[1
]}

机构：

[1] Heilongjiang Univ, Sch Math Sci, Harbin 150080, Peoples R China

[2] Harbin Inst Technol, Sch Mechatron Engn, Harbin 150001, Peoples R China

来源：

PROCEEDINGS OF THE 32ND 2020 CHINESE CONTROL AND DECISION CONFERENCE (CCDC 2020) | 2020年

基金：

中国国家自然科学基金;

关键词：

Distributed average tracking; nonlinear multi-agent systems; neural networks; switching topologies; SUFFICIENT CONDITIONS; REFERENCE SIGNALS; CONSENSUS;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper investigates the average tracking problem for a class of nonlinear multi-agent systems with switching topologies. The control design is developed for switching topologies without requiring the accurate mode with nonlinear dynamics. An adaptive neural-network-based control algorithm is proposed for a team of agents to track the average of multiple time-varying reference signals, of which the dynamics of each subsystem is assumed to be unknown and will be estimated by using adaptive neural network mechanism. Finally, a numerical simulation is given to illustrate the effectiveness of presented consensus protocol.

引用

页码：1756 / 1761

页数：6

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