Semiglobal Consensus of a Class of Heterogeneous Multi-Agent Systems With Saturation
Kexin Liu, Haibo Gu, Wei Wang, Jinhu Lü · IEEE Transactions on Neural Networks and Learning Systems · 2020
This article addresses the leader-following consensus of a class of multi-agent systems (MASs) subjected to saturation. Unlike previous literature, the followers are with heterogeneous dynamics. To solve this problem, we employ the low-gain feedback technique and the parameterized algebraic Riccati equations to design the controllers. For the fixed and switching network topologies, sufficient conditions are put in place to guarantee the semiglobal stability of the consensus error system. Numerical results are also provided to validate the effectiveness of the control design.