Resilient Model-Free Asymmetric Bipartite Consensus for Nonlinear Multi-Agent Systems against DoS Attacks

TL;DR

This paper introduces a resilient, model-free adaptive control approach for nonlinear multi-agent systems to achieve asymmetric bipartite consensus despite DoS attacks, enhancing robustness and stability.

Contribution

It develops a distributed control algorithm combining dynamic linearization, extended state observers, and attack compensation to address asymmetric bipartite consensus under DoS attacks.

Findings

The proposed method successfully achieves consensus under attack conditions.

Numerical example demonstrates robustness and effectiveness.

The approach is applicable to nonlinear multi-agent systems.

Abstract

In this letter, 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 extended discrete state observer to enhance the robustness against unknown dynamics and an attack compensation mechanism to eliminate the adverse effects of DoS attacks, we finally propose a distributed resilient model-free adaptive control algorithm to solve the URABC problem. A numerical example validates the proposed results.