Observer-based consensus tracking of stochastic multi-agent systems via control Lyapunov function

This work discusses the consensus tracking for stochastic multi-agent systems with a leader-follower structure. An observer-based distributed control approach is put forward, leveraging control Lyapunov functions and quadratic programming framework. This control method ensures that each follower can track with the leader's state in the sense of expectation, even under stochastic disturbances. Two observers are given for each follower to observe its own state and the leader's state information, respectively, enabling fully distributed control requirement. Theoretical analysis guarantees the convergence of this control approach, and simulation results validate its effectiveness in achieving consensus tracking.

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Publication Details

Journal
Neural Networks
Published
2025-11-05
DOI
https://doi.org/10.1016/j.neunet.2025.108294
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Observer-based consensus tracking of stochastic multi-agent systems via control Lyapunov function

Boqian Li, Shiping Wen, Tingwen Huang, Zheng Yan et al.
Neural Networks
Distributed Control Multi-Agent Systems
article

Observer-based consensus tracking of stochastic multi-agent systems via control Lyapunov function

Boqian Li, Shiping Wen, Tingwen Huang, Zheng Yan, Ping Qi
article en

Abstract

This work discusses the consensus tracking for stochastic multi-agent systems with a leader-follower structure. An observer-based distributed control approach is put forward, leveraging control Lyapunov functions and quadratic programming framework. This control method ensures that each follower can track with the leader's state in the sense of expectation, even under stochastic disturbances. Two observers are given for each follower to observe its own state and the leader's state information, respectively, enabling fully distributed control requirement. Theoretical analysis guarantees the convergence of this control approach, and simulation results validate its effectiveness in achieving consensus tracking.

Neural NetworksVol. 195
University of Technology Sydney (AU), Shenzhen Technology University (CN), Tongling University (CN)
Openalex Percentile: Top 62%
Distributed Control Multi-Agent Systems
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