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\begin{document}

\title{Resilient Data-Driven Asymmetric Bipartite Consensus for Nonlinear
Multi-Agent Systems against DoS Attacks}



\author[1]{Yi Zhang}%
\author[1]{Yichao Wang}%
\author[1]{Junbo Zhao}%
\author[1]{Shan Zuo}%
\affil[1]{University of Connecticut}%


\vspace{-1em}



  
  \date{May 25, 2024}


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\begin{abstract}
In this article, we study an unified resilient asymmetric bipartite
consensus (URABC) problem for nonlinear multi-agent systems with both
cooperative and antagonistic interactions under denial-of-service (DoS)
attacks. We first prove that the URABC problem is solved by stabilizing
the neighborhood asymmetric bipartite consensus error. Then, we develop
a distributed compact form dynamic linearization method to linearize the
neighborhood asymmetric bipartite consensus error. By using an attack
compensation mechanism to eliminate the adverse effects of DoS attacks
and an extended discrete state observer to enhance the robustness
against unknown dynamics, we finally propose a distributed resilient
model-free adaptive control algorithm to solve the URABC problem. A
numerical example validates the proposed results.%
\end{abstract}%



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