Event-triggered consensus of multi-agent systems with uncertain control gain via distributed fuzzy logic observer

Konghao Xie; Xiujuan Zhao; Shiming Chen; Zheng Zhang; Yuanshi Zheng

doi:10.1016/j.isatra.2025.06.020

Event-triggered consensus of multi-agent systems with uncertain control gain via distributed fuzzy logic observer

ISA Trans. 2025 Jun 24:S0019-0578(25)00320-9. doi: 10.1016/j.isatra.2025.06.020. Online ahead of print.

Authors

Konghao Xie¹, Xiujuan Zhao², Shiming Chen³, Zheng Zhang⁴, Yuanshi Zheng⁵

Affiliations

¹ School of Information Engineering, Jiangxi Science and Technology Normal University, Nanchang 330013, China. Electronic address: 2023010313@jxstnu.edu.cn.
² School of Information Engineering, Jiangxi Science and Technology Normal University, Nanchang 330013, China. Electronic address: xjzhao@jxstnu.edu.cn.
³ School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China. Electronic address: shmchen@ecjtu.jx.cn.
⁴ School of Electrical and Automation Engineering, East China Jiaotong University, Nanchang, 330013, China. Electronic address: zhzhang@ecjtu.edu.cn.
⁵ School of Mechano-electronic Engineering, Xidian University, Xi'an, 710071, China. Electronic address: zhengyuanshi@xidian.edu.cn.

PMID: 40562581
DOI: 10.1016/j.isatra.2025.06.020

Abstract

An event-triggered adaptive backstepping control methodology is proposed to achieve leader-following consensus of uncertain nonlinear high-order multi-agent systems with unknown control gains. In light of the partial observability limitation, adaptive distributed observers are employed to estimate the unobservable states of the leader, whereas local state observers are utilized to reconstruct the states of the followers. By integrating fuzzy logic systems, the unknown nonlinear dynamics are modeled, guaranteeing reliable state prediction in complex and partially observable scenarios. Moreover, the novel relative threshold event-triggered scheme is designed to reduce the frequency of data interactions while ensuring that the tracking error approaches near-zero. Eventually, the effectiveness and superiority of the devised controller are clearly demonstrated through comprehensive simulation results.

Keywords: Adaptive fuzzy control; Distributed observer; Event-triggered mechanism; Multi-agent systems; Output-feedback control.