FDI攻击下非线性多智能体系统分布式无模型自适应控制

为研究受到虚假数据注入攻击的单输入单输出非线性多智能体系统的分布式无模型自适应控制问题，提出了一种新的分布式动态线性化方法, 以获得非线性多智能体的等效线性数据模型。与现有多智能体的分布式无模型自适应控制在控制器设计中有所不同, 本文设计的控制器不需要网络拓扑结构的信息, 仅使用系统的输入输出数据。仿真算例验证了所提出的分布式无模型自适应控制算法可以实现多智能体系统的均方有界趋同控制。算法保证了多智能体系统在受到网络攻击时可以实现趋同控制目标。

Distributed Model-Free Adaptive Control for Nonlinear Multi-Agent Systems with FDI Attacks

The distributed model-free adaptive control (DMFAC) problem for single-input single-output nonlinear multi-agent systems (MASs) with false data injection attacks is studied. A new distributed dynamic linearization method is proposed to obtain an equivalent distributed compact form dynamic linearization data model for nonlinear MASs. Unlike the existing DMFAC results of MASs that the network topology is used in the controller design, the distributed model-free adaptive controller designed in this research uses the input/output data and the controller parameter does not depend on the eigenvalues of the Laplacian matrix. Simulation examples verify that the proposed distributed model-free adaptive control algorithm can acheive bounded consensus control of MASs in the mean square sense. The algorithm can ensure that the multi-agent system achieve the consensus control objective when it is under attacks.