（安徽大学人工智能学院，安徽 合肥 230601）

摘要：针对网络化控制系统存在的网络带宽受限与网络攻击等问题，提出一种动态事件触发（DET）机制。该机制通过采样误差动态调整事件触发参数，并设计相应的触发阈值条件。基于DET机制建立闭环控制系统模型，利用李雅普诺夫稳定性方法推导出闭环控制系统H∞性能的均方渐近稳定充分条件，并融合仿生遗传进化算法与线性矩阵不等式（LMI）设计系统控制器，以提升系统动态性能。以四旋翼无人机系统为例进行仿真实验，验证所提理论在遭受虚假数据注入（FDI）攻击时能够达到预期的系统动态性能，并有效提高了网络通信效率。

关键词：仿生遗传进化算法；H∞控制；动态事件触发机制；线性矩阵不等式；虚假数据注入攻击

中图分类号：TP273               文献标志码：A          文章编号：1674-2605(2026)01-0002-11

DOI：10.12475/aie.20260102                               开放获取

Based on Bio-inspired Genetic Evolution Optimization for Networked H∞ Control of Quadrotor UAVs

ZHANG Yijun  WANG Xuejian  YU Tao

(School of Artificial Intelligence, Anhui University, Hefei 230601, China)

Abstract: A dynamic event-triggered (DET) mechanism is proposed to address issues such as cyber-attacks and bandwidth limitations in networked control systems. This mechanism dynamically adjusts event-triggering parameters based on sampling errors and designs corresponding triggering threshold conditions. A closed-loop control system model is established based on the DET mechanism. Using the Lyapunov stability method, sufficient conditions for mean-square asymptotic stability with H∞ performance of the closed-loop system are derived. Furthermore, a bio-inspired genetic and evolutionary algorithm integrated with linear matrix inequalities (LMIs) is employed to design the system controller, thereby enhancing dynamic performance. Simulation experiments on a quadrotor UAV system demonstrate that the proposed theory achieves the expected dynamic performance under false data injection (FDI) attacks and effectively improves network communication efficiency.

    Keywords:bio-inspired genetic and evolutionary algorithm; H∞ control; dynamic event-triggered mechanism; linear matrix inequality; false data injection attacks