多智能体网络的混杂建模与量化一致性

Hybrid Modeling and Quantized Consensus of Multiagent Networks

针对多智能体网络系统中普遍存在的拓扑变化、信息量化问题,综合考虑了状态量化和拓扑切换的影响.基于一种新的量化器构建了量化反馈作用下多智能体系统的混杂模型,并进一步分析了该混杂系统的有限时间收敛性,给出了一致收敛的时间下界.最后,对不同切换网络拓扑进行计算机仿真,验证所得结果的有效性.

This paper synthetically consider the effects of state quantization and switching topology for multiagent network systems due to the inherent features of time-varying topology and information constraints. Based on a new quantizer, we first construct the hybrid model of multiagent systems under quantized feedback, further analyze the finite-time convergence, and present the lower bound of time to consensus convergence. Finally, a simulation example is provided for different networks with switching topology to validate the theoretical results.