Optimally distributed formation control with obstacle avoidance for mixed-order multi-agent systems under switching topologies

2018 ◽  
Vol 12 (13) ◽  
pp. 1853-1863 ◽  
Author(s):  
Bing Yan ◽  
Peng Shi ◽  
Cheng-Chew Lim ◽  
Chengfu Wu ◽  
Zhiyuan Shi
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.


2017 ◽  
Vol 16 (03) ◽  
pp. 865-880 ◽  
Author(s):  
Jing Yan ◽  
Xinping Guan ◽  
Xiaoyuan Luo ◽  
Cailian Chen

This paper investigates the formation control and obstacle avoidance problem for multi-agent systems (MASs), which aims to coordinate the pursuer agents to capture a mobile target. The target appears at a location randomly and its movement obeys Reactive Rabbit Model. The pursuers and the mobile target can be modeled as a Pursuit-Evasion Game (PEG). During the movement, not all of the pursuer agents can obtain the real-time information of the target. Moreover, the obstacle avoidance makes the formation of pursuer agents a big challenge to encircle the mobile target. In order to tackle these two problems, the formation control and obstacle avoidance algorithm is presented in this paper based on a novel virtual leader-follower strategy and potential functions. The obstacle avoidance problem can then be solved by constructing a velocity potential. The numerical analysis and simulation demonstrate the effectiveness of the proposed algorithm.


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