Estimator‐Based Bipartite Adaptive Event‐Triggered Control for Multiagent Systems With Prescribed Performance

ABSTRACT This article studies the bipartite tracking control problem for multi‐agent systems with a distributed event‐triggered estimator under prescribed performance. Firstly, a distributed estimator is developed, allowing agents not directly connected to the leader direct access to the desired consistency change trajectory. Meanwhile, a dynamic event‐triggered mechanism is introduced to eliminate the need for continuous monitoring of neighboring agents' information. Then, for the controller‐to‐actuator channel, an event‐triggered mechanism based on the error information is designed to save communication resources. Furthermore, a prescribed performance control method with a variety of adjustable preset and non‐monotonic boundaries is introduced into the design of the fuzzy adaptive controller, which avoids the process of redesigning the control after adjusting the boundaries. It is demonstrated that all signals in the closed‐loop systems are semiglobally uniformly ultimately bounded, and Zeno behavior is excluded in the event‐triggered process. Finally, simulation results validate the effectiveness of the proposed scheme.

Authors

Institutions

Publication Details

Journal
International Journal of Robust and Nonlinear Control
Published
2025-08-11
DOI
https://doi.org/10.1002/rnc.70133
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Estimator‐Based Bipartite Adaptive Event‐Triggered Control for Multiagent Systems With Prescribed Performance

Zhechen Zhu, Jinhui Liu, Guangliang Liu, Hongjian Chen
International Journal of Robust and Nonlinear Control
Distributed Control Multi-Agent Systems
article

Estimator‐Based Bipartite Adaptive Event‐Triggered Control for Multiagent Systems With Prescribed Performance

Zhechen Zhu, Jinhui Liu, Guangliang Liu, Hongjian Chen
article en

Abstract

ABSTRACT This article studies the bipartite tracking control problem for multi‐agent systems with a distributed event‐triggered estimator under prescribed performance. Firstly, a distributed estimator is developed, allowing agents not directly connected to the leader direct access to the desired consistency change trajectory. Meanwhile, a dynamic event‐triggered mechanism is introduced to eliminate the need for continuous monitoring of neighboring agents' information. Then, for the controller‐to‐actuator channel, an event‐triggered mechanism based on the error information is designed to save communication resources. Furthermore, a prescribed performance control method with a variety of adjustable preset and non‐monotonic boundaries is introduced into the design of the fuzzy adaptive controller, which avoids the process of redesigning the control after adjusting the boundaries. It is demonstrated that all signals in the closed‐loop systems are semiglobally uniformly ultimately bounded, and Zeno behavior is excluded in the event‐triggered process. Finally, simulation results validate the effectiveness of the proposed scheme.

International Journal of Robust and Nonlinear ControlVol. 36(1)
Nanyang Technological University (SG), Bohai University (CN)
Openalex Percentile: Top 77%
Distributed Control Multi-Agent Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Estimator‐Based Bipartite Adaptive Event‐Triggered Control for Multiagent Systems With Prescribed Performance — Zhechen Zhu, Jinhui Liu, et al. · International Journal of Robust and Nonlinear Control (2025) | TGRS Research Map | TGRS