A Robust Distributed Recurrent Neural Network for Multi-Agent Consensus Control
Yiwei Li, Jiaxin Liu, Lin Yang, Yating Zhang, Kunlin Liu, Ge Zhou, Liangze Yin, Wei Dong
Abstract
Recurrent Neural Networks (RNNs) are widely used in control system due to their dynamic capabilities. However, the control accuracy of RNN-based systems can be compromised by noise interference, and there has been little research on RNN-based control in disturbed multi-agent systems. To address this, we developed an enhanced Distributed RNN (DRNN) structure and proposed a Novel DRNN-based Control Protocol (NDRNN-CP). This enhancement involves introducing a time-delay component, allowing the protocol to adaptively learn noise variation patterns. As a result, the NDRNN-CP effectively resists various periodic noise interferences and achieves more precise control of each agent. Additionally, our optimized activation function ensures that all agents reach consensus within a predefined time. To demonstrate the advantages of NDRNN-CP, we conducted extensive experiments that confirmed its significant improvements in noise signal resistance and convergence performance.
BibTeX
@inproceedings{icassp2025_arobustdistribut,
title = {A Robust Distributed Recurrent Neural Network for Multi-Agent Consensus Control},
author = {Yiwei Li and Jiaxin Liu and Lin Yang and Yating Zhang and Kunlin Liu and Ge Zhou and Liangze Yin and Wei Dong},
booktitle = {ICASSP 2025},
year = {2025}
}