Distributed event-triggered robust adaptive formation control of multi-agent rigid bodies on $$TSE(3)^N$$

The study introduces a distributed robust adaptive event-triggered methodology for formation control of multi-agent rigid bodies within the [Formula: see text] framework, utilizing bearing vector measurements. The rigid body's mass and moment of inertia are treated as unknown but bounded, with an adaptive law designed to estimate these parameters. A time-varying yet bounded disturbance is assumed to influence each rigid body. The robust adaptive control law is adapted to event-triggered scenarios to mitigate communication demands across the network. The communication topology is undirected. A rigorous mathematical proof demonstrates that tracking errors converge exponentially to a residual set. Numerical simulations validate the proposed approach, and a comparative discussion with existing literature is provided.

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Publication Details

Journal
Scientific Reports
Published
2026-04-17
DOI
https://doi.org/10.1038/s41598-026-47678-1
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
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article

Distributed event-triggered robust adaptive formation control of multi-agent rigid bodies on $$TSE(3)^N$$

Manmohan Sharma, Ramalingam Sakthivel, Kuldeep Yadav
Scientific Reports
Distributed Control Multi-Agent Systems
article

Distributed event-triggered robust adaptive formation control of multi-agent rigid bodies on $$TSE(3)^N$$

Manmohan Sharma, Ramalingam Sakthivel, Kuldeep Yadav
article en

Abstract

The study introduces a distributed robust adaptive event-triggered methodology for formation control of multi-agent rigid bodies within the [Formula: see text] framework, utilizing bearing vector measurements. The rigid body's mass and moment of inertia are treated as unknown but bounded, with an adaptive law designed to estimate these parameters. A time-varying yet bounded disturbance is assumed to influence each rigid body. The robust adaptive control law is adapted to event-triggered scenarios to mitigate communication demands across the network. The communication topology is undirected. A rigorous mathematical proof demonstrates that tracking errors converge exponentially to a residual set. Numerical simulations validate the proposed approach, and a comparative discussion with existing literature is provided.

Scientific Reports
Vels University (IN), Vellore Institute of Technology University (IN)
Vellore Institute of Technology, Chennai
Peace, Justice and strong institutions
Openalex Percentile: Top 4%
Distributed Control Multi-Agent Systems
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