Bipartite Output Formation Tracking Control for Discrete-Time Singular Heterogenous Multi-Agent Systems

In this paper, the distributed bipartite output formation control problem of heterogeneous discrete-time singular multi-agent systems (MASs) is studied under the cooperative-competition network. Different from the classical formation control issue, the leader (also called exosystem) is also described by more general discrete-time singular dynamics. Based on the cooperative output regulation theory, the bipartite output formation with two opposing subgroups is realized by reconstructing bipartite tracking error and singular exosystem. To obtain the bipartite state of exosystem, a new exosystem observer associated with discrete-time singular algebraic Riccati equation (ARE) and signed directed topological matrix is designed. The observer coupling gains are introduced to enable the global error system to be included in the stability region of the unit circle. Then under the singular regulator equations and the separation principle, two classes of distributed observed-based controllers via state and output feedbacks are respectively presented for bipartite formation scenarios where the states of followers are known and unknown. Moreover, by redefining the expected relative dynamic positions, the proposed control strategies can be extended to achieve time-varying bipartite output formation. Finally, simulation results are given to verify the feasibility and effectiveness of the proposed algorithms.

Authors

Institutions

Publication Details

Journal
IEEE Transactions on Automation Science and Engineering
Published
2025-01-01
DOI
https://doi.org/10.1109/tase.2025.3584332
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Bipartite Output Formation Tracking Control for Discrete-Time Singular Heterogenous Multi-Agent Systems

Jie Zhang, Da‐Wei Ding, Xiaolei Li, Yao Yao et al.
IEEE Transactions on Automation Science and Engineering
Distributed Control Multi-Agent Systems
article

Bipartite Output Formation Tracking Control for Discrete-Time Singular Heterogenous Multi-Agent Systems

Jie Zhang, Da‐Wei Ding, Xiaolei Li, Yao Yao, Jian-An Wang
article en

Abstract

In this paper, the distributed bipartite output formation control problem of heterogeneous discrete-time singular multi-agent systems (MASs) is studied under the cooperative-competition network. Different from the classical formation control issue, the leader (also called exosystem) is also described by more general discrete-time singular dynamics. Based on the cooperative output regulation theory, the bipartite output formation with two opposing subgroups is realized by reconstructing bipartite tracking error and singular exosystem. To obtain the bipartite state of exosystem, a new exosystem observer associated with discrete-time singular algebraic Riccati equation (ARE) and signed directed topological matrix is designed. The observer coupling gains are introduced to enable the global error system to be included in the stability region of the unit circle. Then under the singular regulator equations and the separation principle, two classes of distributed observed-based controllers via state and output feedbacks are respectively presented for bipartite formation scenarios where the states of followers are known and unknown. Moreover, by redefining the expected relative dynamic positions, the proposed control strategies can be extended to achieve time-varying bipartite output formation. Finally, simulation results are given to verify the feasibility and effectiveness of the proposed algorithms.

IEEE Transactions on Automation Science and EngineeringVol. 22
Yanshan University (CN), Taiyuan University of Science and Technology (CN), Taiyuan University of Technology (CN), University of Science and Technology Beijing (CN)
Shanxi Provincial Key Research and Development Project
Openalex Percentile: Top 83%
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.