JOURNAL ARTICLE

Event-Triggered Bipartite Time-Varying Formation Control for Multiagent Systems With Unknown Inputs

Younan ZhaoFanglai ZhuDezhi Xu

Year: 2022 Journal:   IEEE Transactions on Cybernetics Vol: 53 (9)Pages: 5904-5917   Publisher: Institute of Electrical and Electronics Engineers

Abstract

This article addresses the issues of the bipartite time-varying formation (BTVF) control for multiagent systems (MASs) on signed digraphs. All the designs are performed under the assumptions that the leader and followers suffer from external disturbances, the control input signal of the leader is unreachable to any follower, and the state variables of the followers are unmeasurable. To begin with, an unknown input observer (UIO) is designed for each follower using a traditional interval observer to obtain the state estimates. To realize the BTVF tracking, a distributed consensus error dynamic system is constructed. Furthermore, a distributed unknown input reconstruction method is developed to estimate the multiple disturbances in the consensus error system. Then, an event-triggered BTVF control protocol is proposed which allows two antagonistic time-varying formations to be formed, while excluding Zeno behavior. A simulation of a group of wheeled robots is used to demonstrate the performance of the proposed methods.

Keywords:
Control theory (sociology) Observer (physics) Bipartite graph Computer science Multi-agent system Interval (graph theory) State (computer science) Zeno's paradoxes Control (management) Mathematics Algorithm Artificial intelligence Theoretical computer science Graph

Metrics

38
Cited By
7.93
FWCI (Field Weighted Citation Impact)
44
Refs
0.96
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Distributed Control Multi-Agent Systems
Physical Sciences →  Computer Science →  Computer Networks and Communications
Neural Networks Stability and Synchronization
Physical Sciences →  Computer Science →  Computer Networks and Communications
Adaptive Control of Nonlinear Systems
Physical Sciences →  Engineering →  Control and Systems Engineering

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