Sampled-Data Nonfragile Bipartite Tracking Consensus for Nonlinear Multiagent Systems: Dealing With Denial-of-Service Attacks
研究了在拒绝服务攻击和控制增益波动下，非线性多智能体系统如何通过采样数据实现非脆弱二分跟踪一致性，提出了基于改进Halanay不等式的充分条件，并设计了可计算的控制增益矩阵。
This article examines the nonfragile bipartite tracking consensus issue in the context of sampled-data nonlinear multiagent systems (MASs) undergoing denial-of-service (DoS) attacks and control gain fluctuations, where both cooperative and competitive interactions between the agents over the network are taken into account. During the DoS attacks, with communication services being denied, a halt in data transmission among the agents is experienced, which might result in performance degradation, undesirable oscillatory behavior, or even hinders the agents from performing their intended tasks. Consequently, there emerges a pressing requirement for the analysis and design of a secure bipartite tracking consensus protocol for MASs under the threat of DoS attacks. In pursuit of this goal, a modified Halanay-like inequality is initially established, which provides a basis for us to derive certain sufficient conditions ensuring the MASs to achieve the bipartite tracking consensus, despite the disruptive presence of malicious DoS attacks. Additionally, the control gain matrix can be readily computed by resolving a collection of linear matrix inequalities. For specific scenarios that demand reduced computational complexity, the matrix decoupling method is introduced, enabling a reduction in the dimensionality of the matrix inequalities and, consequently, facilitating its straightforward application to large-scale MASs. This article culminates in a numerical simulation, which is performed to validate the developed results.