Containment analysis and design for general linear multi-agent systems with time-varying delays

2016 ◽  
Vol 173 ◽  
pp. 2062-2068 ◽  
Author(s):  
Xiwang Dong ◽  
Liang Han ◽  
Qingdong Li ◽  
Jian Chen ◽  
Zhang Ren
2019 ◽  
Vol 07 (01) ◽  
pp. 3-13 ◽  
Author(s):  
Wei Xiao ◽  
Jianglong Yu ◽  
Rui Wang ◽  
Xiwang Dong ◽  
Qingdong Li ◽  
...  

Time-varying formation analysis and design problems for general linear multi-agent systems with switching interaction topologies and time-varying delays are studied. Firstly, a consensus-based formation control protocol is constructed using local information of the neighboring agents. An algorithm with three steps is presented to design the proposed formation control protocol. Then, based on linear matrix inequality technique and common Lyapunove–Krasovskii stability theory, sufficient conditions for general linear multi-agent systems with switching topologies and time-varying delays to achieve time-varying formation are given together with a time-varying formation feasibility condition. Finally, a numerical simulation is given to demonstrate the effectiveness of the obtained theoretical results.


Author(s):  
Chengzhi Yuan

This paper addresses the problem of leader-following consensus control of general linear multi-agent systems (MASs) with diverse time-varying input delays under the integral quadratic constraint (IQC) framework. A novel exact-memory distributed output-feedback delay controller structure is proposed, which utilizes not only relative estimation state information from neighboring agents but also local real-time information of time delays and the associated dynamic IQC-induced states from the agent itself for feedback control. As a result, the distributed consensus problem can be decomposed into H∞ stabilization subproblems for a set of independent linear fractional transformation (LFT) systems, whose dimensions are equal to that of a single agent plant plus the associated local IQC dynamics. New delay control synthesis conditions for each subproblem are fully characterized as linear matrix inequalities (LMIs). A numerical example is used to demonstrate the proposed approach.


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