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虚假数据注入式攻击下无人水面船舶自适应神经输出反馈轨迹跟踪控制

祝贵兵 吴晨 马勇

祝贵兵, 吴晨, 马勇. 虚假数据注入式攻击下无人水面船舶自适应神经输出反馈轨迹跟踪控制. 自动化学报, 2024, 50(7): 1−13 doi: 10.16383/j.aas.c220984
引用本文: 祝贵兵, 吴晨, 马勇. 虚假数据注入式攻击下无人水面船舶自适应神经输出反馈轨迹跟踪控制. 自动化学报, 2024, 50(7): 1−13 doi: 10.16383/j.aas.c220984
Zhu Gui-Bing, Wu Chen, Ma Yong. Adaptive neural output feedback trajectory tracking control for USVs under false-data-injection attacks. Acta Automatica Sinica, 2024, 50(7): 1−13 doi: 10.16383/j.aas.c220984
Citation: Zhu Gui-Bing, Wu Chen, Ma Yong. Adaptive neural output feedback trajectory tracking control for USVs under false-data-injection attacks. Acta Automatica Sinica, 2024, 50(7): 1−13 doi: 10.16383/j.aas.c220984

虚假数据注入式攻击下无人水面船舶自适应神经输出反馈轨迹跟踪控制

doi: 10.16383/j.aas.c220984
基金项目: 国家自然科学基金 (52261160383, 52022073, 62073251), 装备预研教育部联合基金 (8091B022239), 海南自然科学基金创新研究团队项目 (722CXTD518), 武汉基础研究知识创新计划 (2022010801010181), 舟山科技局项目 (2022C41006), 武汉理工大学重庆研究院研究项目 (YF2021-12) 资助
详细信息
    作者简介:

    祝贵兵:浙江海洋大学船舶与海运学院副教授. 主要研究方向为鲁棒自适应控制, 神经网络控制, 非线性控制及其在水面船舶上的应用.E-mail: zhuguibing2020@zjou.edu.cn

    吴晨:浙江海洋大学船舶与海运学院硕士研究生. 主要研究方向为船舶导航制导与控制, 神经网络控制和非线性控制.E-mail: ngochen2020@163.com

    马勇:武汉理工大学航运学院教授. 主要研究方向为智能海事保障技术, 船舶智能航行理论与技术. 本文通信作者.E-mail: myongdl@whut.edu.cn

  • 中图分类号: Y

Adaptive Neural Output Feedback Trajectory TrackingControl for USVs Under False-data-injection Attacks

Funds: Supported by National Natural Science Foundation of China (52261160383, 52022073, 62073251), Equipment Preresearch Joint Fund of Ministry of Education (8091B022239), Innovation Research Team Project of Hainan Natural Science Foundation (722CXTD518), Knowledge Innovation Program of Wuhan Basic Research (2022010801010181), Bureau of Science and Technology Project of Zhoushan (2022C41006), and Research Project of Wuhan University of Technology Chongqing Research Institute (YF2021-12)
More Information
    Author Bio:

    ZHU Gui-Bing Associate professor at the School of Naval Architecture and Maritime, Zhejiang Ocean University. His research interest covers robust adaptive control, neural network control, nonlinear control, and their applications to marine surface vessels

    WU Chen  Master student at the School of Naval Architecture and Maritime, Zhejiang Ocean University. Her research interest covers navigation guidance and control of the vessel, neural network control, and nonlinear control

    MA Yong Professor at the School of Navigation, Wuhan University of Technology. His research interest covers intelligent maritime support technology and vessel intelligent navigation theory and technology. Corresponding author of this paper

  • 摘要: 本文主要研究网络环境下无人水面船舶 (Unmanned surface vessels, USVs) 遭受虚假数据注入式 (False-data-injection, FDI) 攻击的跟踪控制问题. 其中, 内部和外部不确定以及输入饱和约束等实际因素均考虑在设计中. 在控制设计过程中, 为避免将船舶速度的攻击信号引入闭环系统, 采用分类重构思想, 构造一种新的神经网络 (Neural network, NN) 状态观测器, 同时重构船舶速度和攻击信号. 进一步, 在backstepping 设计框架下, 利用重构的攻击信号补偿USVs 运动学通道因虚假数据注入式攻击引起的非匹配不确定项. 在动力学设计通道中, 利用自适应神经技术和单参数学习法, 重构由内部和外部不确定组成的复合不确定部分, 进而提出自适应神经输出反馈控制方案. 理论分析表明, 即便在FDI 攻击、内外不确定以及执行器饱和约束的情况下, 所提控制方案能迫使USVs 跟踪给定的参考轨迹. 同时, 仿真和比较结果证实了所提控制方案的有效性和优越性.
  • 图  1  控制方案设计原理图

    Fig.  1  Schematic diagram of control scheme design

    图  2  实际轨迹和参考轨迹图

    Fig.  2  Chart of actual and reference trajectories

    图  3  轨迹误差${{\boldsymbol{S}}}_1$对比图

    Fig.  3  Comparison chart of trajectory errors ${{\boldsymbol{S}}}_1$

    图  4  控制输入${\boldsymbol {\tau}}$对比图

    Fig.  4  Comparison chart of control inputs${\boldsymbol {\tau}}$

    图  5  参数估计值$\hat{{\boldsymbol{\vartheta}}}$

    Fig.  5  Parameter estimation values$\hat{{\boldsymbol{\vartheta}}}$

    图  6  速度估计误差$\tilde{{{\boldsymbol{v}}}}$对比

    Fig.  6  Comparison of velocity estimation errors$\tilde{{{\boldsymbol{v}}}}$

    图  7  位置估计误差$\tilde{{\boldsymbol{\eta}}}$对比

    Fig.  7  Comparison of position estimation errors$\tilde{{\boldsymbol{\eta}}}$

    图  8  有无${\boldsymbol{\sigma}}$时${{\boldsymbol{S}}}_1$对比

    Fig.  8  Comparison of${{\boldsymbol{S}}}_1$with or without${\boldsymbol{\sigma}}$

    图  9  有无${\boldsymbol{\sigma}}$时$\hat{{{\boldsymbol{v}}}}$对比

    Fig.  9  Comparison of$\hat{{{\boldsymbol{v}}}}$with or without${\boldsymbol{\sigma}}$

    表  1  设计参数及初始值

    Table  1  Design parameters and initial values

    指标项式数值
    观测器$ k $12
    $ k_1 $0.1
    $ k_2 $210
    $ k_w $0.1
    $ k_o $6
    ${\boldsymbol{\Lambda}}_o$10$ \times $diag{8, 8, 2}
    控制律$ {\boldsymbol {c}}_1 $diag{1.3, 1.4, 5.0}
    $ {\boldsymbol {c}}_2 $5$ \times $diag{9, 8, 10}
    $\omega_f$30
    $ {\boldsymbol{\Lambda}}_c $diag{5, 5, 5}
    $ {\boldsymbol {k}}_c $0.1$ \times $diag{1, 1, 1}
    $ {\boldsymbol{\varsigma}} $diag{0.01, 0.01, 0.01}
    环境扰动$ {\boldsymbol{\wp}} $diag{−2, −2, −2}
    $ {\boldsymbol{\Upsilon}} $2 × [1.5, 1.5, 1.0]T
    输入饱和限制$ {\boldsymbol{\kappa}} $[0.9, 0.9, 0.9]T
    $ {\boldsymbol{\tau}}_{m} $[10, 10, 5]T
    $Q_0$0.3
    $ {\boldsymbol {k}}_Q $diag{3, 2, 1}
    $ {\boldsymbol {k}}_{w_\tau} $diag{10, 10, 10}
    初始值$ {\boldsymbol{\eta}}(0) $[−1.0, −1.0, 0.1]T
    $ \hat{{\boldsymbol{\eta}}}(0) $[−1.0, −1.0, 0.1]T
    $ {\boldsymbol {v}}(0) $[0, 0, 0]T
    $ {\boldsymbol {S}}(0) $[0.02, 0.02, 0.01]T
    $ \hat{\boldsymbol W}_o(0) $[0.1, 0.1, 0.2]T
    $ \hat{\boldsymbol W}_c(0) $[0.1, 0.1, 0.2]T
    下载: 导出CSV

    表  2  不同攻击下的控制性能对比

    Table  2  Comparison of control performance under different attacks

    指标项式未攻击1 倍攻击4 倍攻击8 倍攻击
    $\int_{0}^{t}\frac{\tau_i(t_f)}{t_f\;+\;0.001}{\rm d}t_f$$ \tau_1 $1.5241.5221.5351.605
    $ \tau_2 $1.2451.2701.4281.742
    $ \tau_3 $0.4760.4770.4840.495
    $ \int_{0}^{t}|S_{1,i}|{\rm d}t_f $$ S_{1,1} $3.3343.2633.0972.986
    $ S_{1,2} $3.3333.3023.2353.191
    $ S_{1,3} $0.4120.4120.4130.412
    $\int_{0}^{t}| \tilde{v}_i|{\rm d}t_f$$ \tilde{v}_1 $0.0720.7583.0526.108
    $ \tilde{v}_2 $0.0740.6322.5195.065
    $ \tilde{v}_3 $0.0080.0180.0930.193
    下载: 导出CSV
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  • 收稿日期:  2022-12-19
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