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首页> 外文期刊>IEEE transactions on industrial informatics >A Data-Driven Attack Detection Approach for DC Servo Motor Systems Based on Mixed Optimization Strategy
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A Data-Driven Attack Detection Approach for DC Servo Motor Systems Based on Mixed Optimization Strategy

机译：基于混合优化策略的DC伺服电机系统数据驱动攻击检测方法

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摘要

This article is concerned with the data-driven attack detection problem for cyber-physical systems with the actuator attacks and measurement noise. In most of existing data-driven detection methods, H-infinity index is used to characterize the sensitivity performance. It is well-known that compared with the H-infinity index, H-index can significantly improve the diagnostic performance. However, the detection system design based on the H-/H-infinity mixed optimization technique has not been solved within the datadriven framework. In this article, a residual generator is constructed from the available input-output (I/O) data. H-infinity and H- indices are defined from the viewpoint of time-domain to characterize the robustness of residual generator against measurement noise and sensitivity to attack signals, respectively. In particular, a novel weighting system, which is expressed as an I/O model, is designed to transform the H-performance into an constraint, and the detection system design problem based on H-/H-infinity mixed optimization technique is finally formulated into a constraint-type optimization one, which can be solved by the classical Lagrange multiplier method. Also, the proposed detection method is applied to a networked dc servo motor system to verify its advantages and effectiveness.
机译：本文涉及具有致动器攻击和测量噪声的网络地理系统的数据驱动攻击检测问题。在现有的大多数数据驱动检测方法中，H-Infinity索引用于表征灵敏度性能。众所周知，与H-Infinity指数相比，H型指数可以显着提高诊断性能。然而，基于H-/ H-Infinity混合优化技术的检测系统设计尚未在DataDRiven框架内解决。在本文中，从可用的输入 - 输出（I / O）数据构建剩余发电机。从时域的角度定义了H-Infinity和H-Indice，以表征残留发电机的稳健性，分别对测量噪声和对攻击信号的敏感性。特别地，设计为I / O模型的新型加权系统被设计为将H-Perstaply转换为约束，并且最终制定了基于H-/ H-Infinity混合优化技术的检测系统设计问题进入约束类型优化，可以通过经典拉格朗日乘法器方法解决。此外，所提出的检测方法应用于网络DC伺服电机系统以验证其优点和有效性。

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