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基于机器学习的信息物理系统安全控制

刘坤 马书鹤 马奥运 张淇瑞 夏元清

刘坤, 马书鹤, 马奥运, 张淇瑞, 夏元清. 基于机器学习的信息物理系统安全控制. 自动化学报, 2021, 47(6): 1273−1283 doi: 10.16383/j.aas.c190352
引用本文: 刘坤, 马书鹤, 马奥运, 张淇瑞, 夏元清. 基于机器学习的信息物理系统安全控制. 自动化学报, 2021, 47(6): 1273−1283 doi: 10.16383/j.aas.c190352
Liu Kun, Ma Shu-He, Ma Ao-Yun, Zhang Qi-Rui, Xia Yuan-Qing. Secure control for cyber-physical systems based on machine learning. Acta Automatica Sinica, 2021, 47(6): 1273−1283 doi: 10.16383/j.aas.c190352
Citation: Liu Kun, Ma Shu-He, Ma Ao-Yun, Zhang Qi-Rui, Xia Yuan-Qing. Secure control for cyber-physical systems based on machine learning. Acta Automatica Sinica, 2021, 47(6): 1273−1283 doi: 10.16383/j.aas.c190352

基于机器学习的信息物理系统安全控制

doi: 10.16383/j.aas.c190352
基金项目: 国家自然科学基金(61873034, 61503026, 61836001), 北京自然科学基金(4182057), 国家自然科学基金重大国际(地区)合作项目(61720106010), 北京市智能物流系统协同创新中心开放课题(BILSCIC-2019KF-13), 北京理工大学研究生创新项目(2019CX20031)资助
详细信息
    作者简介:

    刘坤:北京理工大学自动化学院教授. 主要研究方向为网络化控制理论与应用, 复杂网络控制与安全. 本文通信作者. E-mail: kunliubit@bit.edu.cn

    马书鹤:北京理工大学自动化学院硕士研究生. 主要研究方向为攻击检测, 安全控制, 机器学习. E-mail: mashuhehe@163.com

    马奥运:北京理工大学自动化学院博士研究生. 主要研究方向为模型预测控制, 优化控制. E-mail: maaoyun92@gmail.com

    张淇瑞:北京理工大学自动化学院博士研究生. 主要研究方向为信息物理系统的安全控制, 最优化控制. E-mail: qiruizhang@bit.edu.cn

    夏元清:北京理工大学自动化学院教授. 主要研究方向为云控制, 云数据中心优化调度管理, 智能交通, 模型预测控制, 自抗扰控制, 飞行器控制和空天地一体化网络协同控制. E-mail: xia_yuanqing@bit.edu.cn

Secure Control for Cyber-physical Systems Based on Machine Learning

Funds: Supported by National Natural Science Foundation of China (61873034, 61503026, 61836001), Beijing Natural Science Foundation (4182057), Major International (Regional) Joint Research Project of National Natural Science Foundation of China (61720106010), the Open Subject of Beijing Intelligent Logistics System Collaborative Innovation Center (BILSCIC-2019KF-13), and Graduate Technological Innovation Project of Beijing Institute of Technology (2019CX20031)
More Information
    Author Bio:

    LIU Kun Professor at the School of Automation, Beijing Institute of Technology. His research interest covers theory and applications of networked control, and control and security of complex networked systems. Corresponding author of this paper

    MA Shu-He Master student at the School of Automation, Beijing Institute of Technology. Her research interest covers attack detection, secure control, and machine learning

    MA Ao-Yun  Ph.D. candidate at the School of Automation, Beijing Institute of Technology. His research interest covers model predictive control and optimal control

    ZHANG Qi-Rui Ph.D. candidate at the School of Automation, Beijing Institute of Technology. His research interest covers secure control of cyber-physical systems and optimal control

    XIA Yuan-Qing Professor at the School of Automation, Beijing Institute of Technology. His research interest covers cloud control, cloud data center optimization scheduling and management, intelligent transportation, model predictive control, active disturbance rejection control, flight control, and networked cooperative control for integration of space, air and earth

  • 摘要: 研究了控制信号被恶意篡改的信息物理系统的安全控制问题. 首先, 提出一种改进果蝇优化核极限学习机算法(Kernel extreme learning machine with improved fruit fly optimization algorithm, IFOA-KELM)对攻击信号进行重构. 然后, 将所得重构信号作为系统扰动加以补偿, 进而设计模型预测控制策略, 并给出了使被控系统是输入到状态稳定的条件. 另外, 本文从攻击者角度建立优化模型得到最优攻击策略用以生成足够的受攻击数据, 基于此数据, 来训练改进果蝇优化核极限学习机算法. 最后, 使用弹簧−质量−阻尼系统进行仿真, 验证了改进果蝇优化极限学习机算法和所提安全控制策略的有效性.
  • 图  1  遭受攻击的信息物理系统框图

    Fig.  1  The diagram of the CPS under cyber attack

    图  2  极限学习机结构图

    Fig.  2  The structure of ELM

    图  3  FOA优化参数

    Fig.  3  The optimization parameter of FOA

    图  4  IFOA寻优过程

    Fig.  4  The optimization process of IFOA

    图  5  弹簧−质量−阻尼系统结构图

    Fig.  5  The structure of spring-quality-damping system

    图  6  系统状态轨迹

    Fig.  6  The state of the system

    图  7  训练样本

    Fig.  7  The training sample

    图  8  IFOA-KELM测试样本绝对误差

    Fig.  8  The error between the real attack and the attack learned by IFOA-KELM

    图  11  LSSVM测试样本绝对误差

    Fig.  11  The error between the real attack and the attack learned by LSSVM

    图  9  FOA-KELM测试样本绝对误差

    Fig.  9  The error between the real attack and the attack learned by FOA-KELM

    图  10  PSO-BP测试样本绝对误差

    Fig.  10  The error between the real attack and the attack learned by PSO-BP

    图  12  IFOA和FOA最优适应度值变化曲线

    Fig.  12  The Smellbest of IFOA and FOA

    图  13  受攻击系统引入MPC前后的状态轨迹

    Fig.  13  The state trajectory of the attacked system with MPC and without MPC

    图  14  真实攻击信号与重构攻击信号之间的误差

    Fig.  14  The error between the real attack and the learned

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  • 收稿日期:  2019-05-10
  • 录用日期:  2019-10-21
  • 刊出日期:  2021-06-10

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