对随机发生的FDIA的二维马尔科夫跳跃系统进行基于协议的状态估计
IEEE transactions on cybernetics
|September 3, 2025
概括
这项研究引入了一种新方法,用于对错误数据注入攻击的二维马尔科夫跳跃系统的状态估计. 这种方法提高了系统的稳定性和适应性,使其具有可靠的性能.
科学领域:
- 控制系统工程
- 信号处理
- 系统中的网络安全
背景情况:
- 状态估计对于二维马尔科夫跳跃系统至关重要.
- 随机发生的虚假数据注入攻击 (FDIA) 对系统完整性和性能构成重大威胁.
- 现有的方法可能缺乏对动态网络条件和复杂攻击的稳定性.
研究的目的:
- 在FDIA下开发一个强大的2D马尔科夫跳跃系统状态估计方法.
- 在不确定的网络环境中提高系统的适应性和性能.
- 为了保证稳定性和噪声减弱尽管恶意数据注入.
主要方法:
- 提出了一个新的概率多间隔事件触发机制 (PMIETP),结合了分间隔值和概率模型.
- 开发了一个基于时间变化的和机制 (TVSM) 的估计器,具有适应值来处理异常数据.
- 利用粒子群优化 (PSO) 来优化设计参数,减少线性矩阵不等式 (LMI) 条件中的保守性.
- 根据Lyapunov稳定性理论,获得了足够的标准,用于平均方位的非对称稳定性和噪声减弱.
主要成果:
- 拟议的与TVSM集成的PMIETP有效地减轻了FDIA的影响.
- 这种方法显示了对不同网络条件的更强的适应性和更强的估计稳定性.
- 数字模拟证实了拟议方法对现有技术的有效性和优势.
结论:
- 基于PMIETP和TVSM的新型状态估计方法为FDIA下的二维马尔科夫跳转系统提供了强大的解决方案.
- 该方法确保了系统稳定性和性能保证,在实际应用中提供了显著的优势.
- 该研究强调了适应机制和优化算法的潜力,以提高控制系统的安全性.
更多相关视频
10:20Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
Published on: September 5, 2019
8.3K
09:17Structure-Based Simulation and Sampling of Transcription Factor Protein Movements along DNA from Atomic-Scale Stepping to Coarse-Grained Diffusion
Published on: March 1, 2022
3.2K
相关概念视频
BIBO stability of continuous and discrete -time systems
511
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
511
Linear time-invariant Systems
403
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
403
Propagation of Uncertainty from Systematic Error
882
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
882
Propagation of Uncertainty from Random Error
1.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
1.1K
State Space Representation
283
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Consider an RLC circuit, a...
283
Linear Approximation in Time Domain
124
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
124
