分布式电网CPS 系统数据攻击下的状态估计

电力物理网络通过构建信息网络进行优化调控并构成信息物理融合系统, 实现大规模分布式系统的优化控制, 随之而来的问题是病毒、黑客入侵、拒绝服务等来自信息网络的威胁, 导致物理系统恶意破坏. 鉴于此, 以攻击可检测为前提, 建立攻击信号下的电力系统分布式动态模型, 设计动态状态估计器检测受攻击的信号, 并估计其原始信号. 最后通过3 机9 节点分布式电网系统仿真实验验证了所设计的状态估计器对于数据攻击检测的有效性.

     

DoS攻击下信息物理系统的分布式协同控制

针对具有复杂非线性动态的信息物理系统(CPS)在拒绝服务(Do S)攻击下的跟踪协同控制问题,提出了一种分布式间歇控制方法。首先,将CPS建模为一类二阶非线性多智能体系统,通过在控制器中引入间歇机制来设计分布式控制器。其次,使用驻留时间分析方法对Do S攻击的持续时间和频率进行了详细分析,以保证DoS攻击下系统可以实现跟踪协同。此外,通过构造合适的李雅普诺夫函数和利用数学归纳法,分析所得误差系统的稳定性,最终得到跟踪协同的充分条件。值得注意的是,该结果为控制增益的设计提供了更高的灵活性。最后,通过仿真验证了所提理论结果的有效性和可靠性。     

网络攻击下的信息物理系统安全状态估计研究综述

近年来,信息物理系统在工业界的广泛应用引起了人们对系统安全问题的极大关注.信息物理系统对通信网络的深度依赖,使得网络攻击成为其中最为严峻的威胁之一,特别是那些能够干扰系统状态认知的攻击,因此,安全状态估计(即在遭受攻击时正确估计系统状态)已成为各界广泛关注的安全问题之一.此文旨在总结网络攻击下信息物理系统安全状态估计研究的进展.首先,介绍典型的网络攻击,并详细阐述在稀疏攻击下的安全状态估计问题.其次,探讨集中式安全状态估计和分布式安全状态估计的研究现状.在考虑稀疏攻击下安全状态估计问题的难点时,关键在于如何快速找到受到攻击的信道集合(这可能涉及到高计算复杂度).因此,将安全状态估计方法分为遍历搜索和非遍历搜索两大类,并对现有方法的优缺点进行归纳总结和详细阐述.然后,介绍稀疏攻击下信息物理系统安全状态能观性分析的研究现状.现有的研究结果表明:增加检测机制或先验知识可以缓解在稀疏攻击下安全状态估计所需的基础冗余度要求;同时,通过区分攻击和故障,也能有效降低传感器冗余度要求.最后,对信息物理系统安全状态估计仍然存在的问题进行展望,并提出一些可能的解决方向.     

基于随机有限集的信息物理系统状态估计北大核心CSCD

状态估计一直是信息物理系统(CPS)的基本问题之一。目前,状态估计主要采用以卡尔曼滤波及其扩展形式等为代表的随机过程方法。不同于已有研究,从信息融合的角度,提出了一种基于随机有限集的状态估计方法。通过将系统状态和传感器观测分别建模为随机有限集,并采用集贝叶斯滤波器及其粒子实现迭代求解,实现了对CPS状态的精确估计。最后,仿真结果验证了所提方法的可行性。     

恶意攻击下基于分布式稀疏优化的安全状态估计

恶意生成的量测攻击信号是导致信息物理系统(Cyber-physical system,CPS)探测失效的主要原因,如何有效削弱其影响是实现精准探测、跟踪与感知的关键问题.分布式传感器网络(Distributed sensor network,DSN)依靠多传感器协作与并行处理突破单一监测节点的任务包线,能够显著提升探测...     

网络攻击下的信息物理系统安全性研究综述

随着信息物理系统在现代工业和制造业中的广泛应用,其安全性逐渐成为关系社会健康发展的重要因素.由于信息物理系统内部物理设备和通信网络的深度融合,网络攻击对系统安全的威胁日益凸显.首先,从攻击者角度总结各类网络攻击的特点,揭示系统在不同攻击下的脆弱性;其次,针对不同网络攻击的特性,从防御者角度对信息物理系统的安全状态估计、攻击检测和安全控制进行介绍,并阐述各防御策略的主要应用场景和优势;最后,对信息物理系统安全性研究面临的主要挑战进行展望.     

稀疏恶意攻击下的信息物理系统的"PID"型性能和安全控制

针对遭受稀疏恶意攻击的离散时间线性系统,本文研究其安全控制问题.假设恶意攻击者受有限资源的约束,仅能操控远程控制器和执行器之间的若干通信通道.对于设计者来说,并不知道哪些通道受到攻击,哪些通道没有受到攻击.本文提出了一种新的安全的远程控制方法,它由控制律、切换函数和选择机制构成.选择机制为控制律提供合适的反馈增益,并产...     

扩展卡尔曼滤波在受到恶意攻击系统中的状态估计

设计了一种分布式扩展卡尔曼滤波器(Extended Kalman filter, EKF), 对非线性目标状态进行估计.在设计过程中, 对滤波误差上界进行优化, 获得了最优滤波增益.此外, 在通信过程中, 考虑恶意攻击信号的同时引入了分布式事件触发机制, 使得系统在保持一定的估计精度的情况下节省通信资源.最后, 以室内的机器人定位问题为例, 验证了提出的滤波器的有效性.     

工业信息物理系统数据注入攻击的事件触发弹性控制

工业信息物理系统(ICPS)恶劣的工业环境使得无线通信网络的通信资源有限, 且通信网络的脆弱性使得系统的稳定性容易受到数据注入攻击的破坏. 本文基于输入到状态理论, 采用H1观测器、预测器和事件触发器建立基于事件触发的ICPS模型来降低通信资源的利用率, 并构造基于改进卡尔曼滤波器的攻击补偿模块进行攻击补偿, 攻击补偿模块的参数随着攻击者攻击策略不同而进行改变, 实现对数据注入攻击的弹性控制. 用matlab软件进行仿真实验, 以倒立摆作为被控对象, 模拟基于事件触发的ICPS模型受到数据注入攻击时系统的稳定性能. 仿真结果表明, 事件触发控制策略和基于改进卡尔曼滤波器的攻击补偿模块的组合不仅能够保证系统期望的稳定性, 还能有效减少数据通过无线网络传输的次数.     

工业信息物理系统的攻击建模研究

无线通信网络的脆弱性使得工业信息物理系统易遭受各类网络攻击.为了更深入地了解不同网络攻击的特征进而建立有效的防御措施,构建一种线性时不变离散系统的工业信息物理系统结构;深入研究信息物理系统攻击者攻击空间及攻击者攻击模型,采用控制理论方法研究攻击空间模型的模型知识、披露资源和破坏资源的数学表达;对拒绝服务攻击、重放攻击、虚假数据注入攻击3种典型网络攻击的基本特性,以及对应攻击下攻击模型的表现形式进行分析.通过Simulink/Truetime仿真工具对破坏性和隐蔽性性能进行仿真实验.结果表明,所研究的攻击空间模型及攻击者攻击模型能够有效地描述网络攻击的攻击特性.     

Distributed Secure State Estimation of Multi-Agent Systems Under Homologous Sensor Attacks

This paper addresses the problem of distributed secure state estimation for multi-agent systems under homologous sensor attacks. Two types of secure Luenberger-like distributed observers are proposed to estimate the system state and attack signal simultaneously. Specifically, the proposed two observers are applicable to deal with the cases in the presence and absence of time delays during network communication. It is also shown that the proposed observers can ensure the attack estimations from different agents asymptotically converge to the same value. Sufficient conditions for guaranteeing the asymptotic convergence of the estimation errors are derived. Simulation examples are finally provided to demonstrate the effectiveness of the proposed results.      

拒绝服务攻击下基于UKF的智能电网动态状态估计研究

针对连续拒绝服务（Denial of service,DoS）攻击导致量测数据丢失使得动态状态估计失效、进而破坏智能电网安全经济运行问题,本文提出了一种适用拒绝服务攻击的改进无迹卡尔曼滤波（Unscented Kalman filter,UKF）方法,以进行智能电网动态状态估计.首先,分析拒绝服务攻击引起数据丢包特性并设计了数据补偿策略,以重构电力系统动态模型;然后,结合Holt's双参数指数平滑和无迹卡尔曼滤波方法,构造了融合补偿信息的新状态估计方程,并进一步基于估计误差协方差矩阵推导了状态增益更新方法,从而得到了无迹卡尔曼滤波动态状态估计新方法.最后,针对IEEE 30和118节点系统进行仿真,验证了所提方法的可行性和有效性.     

传感器网络中的分布式滚动时域状态估计

利用分布式滚动时域方法对无线传感器网络的状态估计问题进行研究,给出了基于量化测量值的滚动时域估计算法。在无线传感器网络的环境下处理分布式状态估计问题时,减少通信的成本是非常重要的一个环节,需要将观测值量化后再传送。以往的滚动时域估计方法无法处理量化观测值的状态估计问题,而本文的方法考虑了最严格的观测值量化情况即传感器只发送一个比特至融合中心的状态估计问题。与其它传感器网络中的状态估计方法相比,该方法减少了每一步的计算量。仿真结果验证了该算法的有效性。     

Finite‐Time H∞ State Estimation for Discrete‐Time Switched Control Systems Under Asynchronous Switching

The Luenberger observer technique has long been used for reliable state estimation of feedback control systems. In this paper it is employed for asynchronously switched control systems. The theory of finite‐time stability (FTS) and finite‐time boundedness (FTB) is incorporated, which is a relatively new concept offering open and challenging problems, especially in the field of robust state estimation for switched control systems. Many real‐world systems are supposed to run for a fixed interval of time and hence the design of finite‐time state observers has more significance from the point of view of many engineering applications. This investigation considers asynchronous switching between observer and physical systems, which accounts for inherent delays in practical applications caused by measurement and sensing mechanisms, and crafts a more practical and accurate design method. Furthermore, performance index which is a vital technique for robust system design against external disturbances is integrated in the design procedure.     

A Sensorless State Estimation for A Safety-Oriented Cyber-Physical System in Urban Driving: Deep Learning Approach

In today's modern electric vehicles,enhancing the safety-critical cyber-physical system(CPS)'s performance is necessary for the safe maneuverability of the vehicle.As a typical CPS,the braking system is crucial for the vehicle design and safe control.However,precise state estimation of the brake pressure is desired to perform safe driving with a high degree of autonomy.In this paper,a sensorless state estimation technique of the vehicle's brake pressure is developed using a deep-learning approach.A deep neural network(DNN)is structured and trained using deep-learning training techniques,such as,dropout and rectified units.These techniques are utilized to obtain more accurate model for brake pressure state estimation applications.The proposed model is trained using real experimental training data which were collected via conducting real vehicle testing.The vehicle was attached to a chassis dynamometer while the brake pressure data were collected under random driving cycles.Based on these experimental data,the DNN is trained and the performance of the proposed state estimation approach is validated accordingly.The results demonstrate high-accuracy brake pressure state estimation with RMSE of 0.048 MPa.     

基于一种改进型逆凸不等式的混合攻击下多区域时滞电力系统的分布式控制

本文研究了混合攻击下多区域时滞电力系统的分布式协调控制问题。首先,考虑了一种基于混合攻击下（包括拒绝服务攻击和欺骗攻击）的多区域时滞电力系统,在拒绝服务攻击下,时滞电力系统的各个区域之间的通信将恶化为一种切换残余拓扑结构,它的时间特性建模成了模型依赖的平均停留时间的模型。在以负反馈控制为目标的欺骗攻击下,将其建模成了基于符号函数的模型。然后,为了在低惯性和混合攻击下获得时滞电力系统的弹性性能,采用了负荷频率控制与虚拟惯性控制相结合的弹性分布式策略,并将时滞电力系统的负荷频率控制问题转换为切换非线性切换系统控制问题。再通过采用合适的Lyapunov-Krasovskii泛函、改进型逆凸不等式、线性矩阵不等式技术以及切换系统方法,得到了多区域时滞电力系统的稳定性定理。并基于此定理,设计了负荷频率控制和虚拟惯性控制下的分布式控制增益。最后,建立仿真,针对三区域电力系统进行了仿真,仿真结果体现了本文所提出来的稳定性定理和设计的分布式控制增益的优势和有效性。     

A Resilient Control Strategy for Cyber-Physical Systems Subject to Denial of Service Attacks: A Leader-Follower Set-Theoretic Approach

Multi-agent systems are usually equipped with open communication infrastructures to improve interactions efficiency, reliability and sustainability. Although technologically cost-effective, this makes them vulnerable to cyber-attacks with potentially catastrophic consequences. To this end, we present a novel control architecture capable to deal with the distributed constrained regulation problem in the presence of time-delay attacks on the agents’ communication infrastructure. The basic idea consists of orchestrating the interconnected cyber-physical system as a leader-follower configuration so that adequate control actions are computed to isolate the attacked unit before it compromises the system operations. Simulations on a multi-area power system confirm that the proposed control scheme can reconfigure the leader-follower structure in response to denial of-service (DoS) attacks.      

Sliding Mode Control for Nonlinear Markovian Jump Systems Under Denial-of-Service Attacks

This paper investigates the sliding mode control (SMC) problem for a class of discrete-time nonlinear networked Markovian jump systems (MJSs) in the presence of probabilistic denial-of-service (DoS) attacks. The communication network via which the data is propagated is unsafe and the malicious adversary can attack the system during state feedback. By considering random Denial-of-Service attacks, a new sliding mode variable is designed, which takes into account the distribution information of the probabilistic attacks. Then, by resorting to Lyapunov theory and stochastic analysis methods, sufficient conditions are established for the existence of the desired sliding mode controller, guaranteeing both reachability of the designed sliding surface and stability of the resulting sliding motion. Finally, a simulation example is given to demonstrate the effectiveness of the proposed sliding mode control algorithm.      

Sampled‐Data Approach to State Estimation Performance for Heterogeneous Distributed System with Fault

This paper considers the state estimation performance for heterogeneous distributed system with fault based on sampled‐data measurement. Firstly, a performance index for distributed state estimation is proposed. Based on the sampled‐data measurement of the heterogeneous distributed system, distributed state estimators are constructed. It is proved that by analyzing the distributed state estimation error systems, the required performance index for overall state estimation can be obtained using linear matrix inequality method. Finally, a simulation example is used to illustrate the effectiveness of the proposed theoretical scheme.