Observer-based distributed consensus for multi-agent systems with directed networks and input saturation - Yu, Shuzhen; Yu, Zhiyong; Jiang, Haijun; Mei, Xuehui (Neurocomputing (Amsterdam), 2021)
In this paper, we devise a distributed protocol to study the consensus of multi-agent systems (MASs) with input saturation over directed networks. Firstly, a low-gain feedback control method is applied to overcome the saturation constraints, and an observation system without saturation constraints is designed. The semiglobal consensus condition over directed strongly connected networks is developed by using Ricatti equation. Secondly, a novel of tree-type error scheme is proposed to solve the consensus of MASs with general directed spanning tree networks. Combination with inequality techniques and Lyapunov stability theory, some criteria for achieving semiglobal consensus are obtained. In addition, the semiglobal consensus of MASs with directed switching networks is further considered, in which the network topologies only need to contain a directed spanning tree in some time intervals. Some related conditions are derived to ensure the consensus. Finally, some numerical simulations are given to demonstrate the validity of the theoretical results.