Neural-Network-Based Adaptive Leader-Following Control for Multiagent Systems with Uncertainties

被引：307

作者：

Cheng, Long ^{[1
]}

Hou, Zeng-Guang ^{[1
]}

Tan, Min ^{[1
]}

Lin, Yingzi ^{[2
]}

Zhang, Wenjun ^{[3
]}

机构：

[1] Chinese Acad Sci, Inst Automat, Key Lab Complex Syst & Intelligence Sci, Beijing 100190, Peoples R China

[2] Northeastern Univ, Coll Engn, Dept Mech & Ind Engn, Boston, MA 02108 USA

[3] Univ Saskatchewan, Dept Mech Engn, Saskatoon, SK S7N 5A9, Canada

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS | 2010年 / 21卷 / 08期

基金：

中国国家自然科学基金;

关键词：

Adaptive; leader-following control; multiagent system; neural networks; uncertainty; MIMO NONLINEAR-SYSTEMS; CONSENSUS; DESIGN;

D O I：

10.1109/TNN.2010.2050601

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

A neural-network-based adaptive approach is proposed for the leader-following control of multiagent systems. The neural network is used to approximate the agent's uncertain dynamics, and the approximation error and external disturbances are counteracted by employing the robust signal. When there is no control input constraint, it can be proved that all the following agents can track the leader's time-varying state with the tracking error as small as desired. Compared with the related work in the literature, the uncertainty in the agent's dynamics is taken into account; the leader's state could be time-varying; and the proposed algorithm for each following agent is only dependent on the information of its neighbor agents. Finally, the satisfactory performance of the proposed method is illustrated by simulation examples.

引用

页码：1351 / 1358

页数：10

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