Time-varying Nonholonomic Robot Consensus Formation Using Model Predictive Based Protocol With Switching Topology

Author(s):  
Hanzhen Xiao ◽  
C.L. Philip Chen
2011 ◽  
Vol 48-49 ◽  
pp. 724-729
Author(s):  
Hui Yu ◽  
Yi Zhang ◽  
Gao Yang Liu

This paper is devoted to the study of consensus problem of multi-agent systems with a time-varying reference state in directed networks with both switching topology and time-delay. Stability analysis is performed based on a proposed Lyapunov–Krasovskii function. Sufficient conditions based on linear matrix inequalities (LMIs) are given to guarantee that multi-agent consensus on a time-varying reference state can be achieved under arbitrary switching of the network topology even if the network communication is affected by time-delay. These consensus algorithms are also extended to consensus formation among the agents. Finally, simulation example is given to validate our theoretical results.


Symmetry ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 941
Author(s):  
Tianhao Sun ◽  
Huiying Liu ◽  
Yongming Yao ◽  
Tianyu Li ◽  
Zhibo Cheng

In this paper, the time-varying formation tracking problem of the general linear multi-agent system is discussed. A distributed formation tracking protocol based on Riccati inequalities with adaptive coupling weights among the follower agents and the leader agent is designed for a leader-following multi-agent system under fixed and switching topologies. The formation configuration involved in this paper is expressed as a bounded piecewise continuously differentiable vector function. The follower agents will achieve the desired formation tracking trajectory of the leader. In traditional static protocols, the coupling weights depend on the communication topology and is a constant. However, in this paper, the coupling weights are updated by the state errors among the neighboring agents. Moreover, the stability analysis of the MAS under switching topology is presented, and proves that the followers also could achieve pre-specified time-varying formation, if the communication graph is jointly connected. Two numerical simulations indicate the capabilities of the algorithms.


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