Distributed Online Gradient Boosting on Data Stream over Multi-agent Networks

2021 ◽  
pp. 108253
Author(s):  
Xibin An ◽  
Chen Hu ◽  
Gang Liu ◽  
Haoshen Lin
Keyword(s):  
Data Stream ◽  
Gradient Boosting ◽  
Multi Agent ◽  
Agent Networks

New methods to realize the cluster consensus for multi‐agent networks

2019 ◽  
Vol 22 (6) ◽  
pp. 2549-2557
Author(s):  
Xing Guo ◽  
Jinling Liang ◽  
Habib M. Fardoun
Keyword(s):  
New Methods ◽  
Multi Agent ◽  
Cluster Consensus ◽  
Agent Networks

Privacy-preserving weighted average consensus and optimal attacking strategy for multi-agent networks

2021 ◽  
Vol 358 (6) ◽  
pp. 3033-3050
Author(s):  
Aijuan Wang ◽  
Wanping Liu ◽  
Tiehu Li ◽  
Tingwen Huang
Keyword(s):  
Weighted Average ◽  
Privacy Preserving ◽  
Average Consensus ◽  
Multi Agent ◽  
Agent Networks

Trust in multi-agent networks: From self-centered to team-oriented

2017 ◽  
Author(s):  
Tina Setter ◽  
Andrea Gasparri ◽  
Magnus Egerstedt
Keyword(s):  
Multi Agent ◽  
Agent Networks

Finite-Time Consensus for Leader-Following Second-Order Multi-Agent Networks

2012 ◽  
Vol 59 (11) ◽  
pp. 2646-2654 ◽  
Author(s):  
Zhi-Hong Guan ◽  
Feng-Lan Sun ◽  
Yan-Wu Wang ◽  
Tao Li
Keyword(s):  
Finite Time ◽  
Second Order ◽  
Leader Following ◽  
Multi Agent ◽  
Agent Networks

scholarly journals Formation Tracking Control for Multi-Agent Networks with Fixed Time Convergence via Terminal Sliding Mode Control Approach

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1416
Author(s):  
Guang-Hui Xu ◽  
Meng Li ◽  
Jie Chen ◽  
Qiang Lai ◽  
Xiao-Wen Zhao
Keyword(s):  
Tracking Control ◽  
Sliding Mode ◽  
Fixed Time ◽  
Stability Theorem ◽  
Control Task ◽  
Terminal Sliding Mode ◽  
Formation Tracking ◽  
Multi Agent ◽  
Error System ◽  
Agent Networks

This paper investigates formation tracking control for multi-agent networks with fixed time convergence. The control task is that the follower agents are required to form a prescribed formation within a fixed time and the geometric center of the formation moves in sync with the leader. First, an error system is designed by using the information of adjacent agents and a new control protocol is designed based on the error system and terminal sliding mode control (TSMC). Then, via employing the Lyapunov stability theorem and the fixed time stability theorem, the control task is proved to be possible within a fixed time and the convergence time can be calculated by parameters. Finally, numerical results illustrate the feasibility of the proposed control protocol.

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