Paper Info
Title
Asymptotic Consensus Tracking Of Uncertain Multi-Agent Systems With A High-Dimensional Leader: A Neuro-Adaptive Approach
Abstract
In this note, the asymptotic consensus tracking problem is addressed for uncertain multi-agent systems (MASs) with undirected communication topologies and a high-dimensional leader, where the uncertainties may contain unmodeled dynamics and external disturbance which are prior unknown. To remove the effect of high-dimensional leader, an observer based compensation controller is firstly designed. A neural-adaptive based feedback controller is then designed. Note that the feedback term contains a discontinuous controller which is used to eliminate the effect of imprecise approximation of neural network. Furthermore, if the leader is assumed to be globally reachable, it is shown that asymptotic consensus tracking is achieved in MAS by choosing appropriate control parameters. The obtained theoretical result is finally validated by simulation.
Year
DOI
Venue
2018
10.1109/IECON.2018.8591072
IECON 2018 - 44TH ANNUAL CONFERENCE OF THE IEEE INDUSTRIAL ELECTRONICS SOCIETY
Keywords
Field
DocType
neuro-adaptive, high-dimensional leader, multi-agent system, asymptotic consensus tracking.
Control theory,Feedback controller,Control theory,Multi-agent system,Symmetric matrix,Control engineering,Network topology,Engineering,Artificial neural network,Observer (quantum physics)
Conference
ISSN
Citations 
PageRank 
1553-572X
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Peijun Wang1505.73
Xinghuo Yu23954300.63
Wenwu Yu34340185.95
Guanghui Wen42100113.74
Lv Jinhu52906244.29