Resilient Data-Driven Asymmetric Bipartite Consensus for Nonlinear Multi-Agent Systems against DoS Attacks

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.