虛假數據注入式攻擊下無(wú)人水面船舶自適應神經(jīng)輸出反饋軌跡跟蹤控制
doi: 10.16383/j.aas.c220984
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浙江海洋大學(xué)船舶與海運學(xué)院 舟山 316022
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武漢理工大學(xué)水路交通控制全國重點(diǎn)實(shí)驗室 武漢 430063
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武漢理工大學(xué)航運學(xué)院 武漢 430063
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武漢理工大學(xué)國家水運安全工程技術(shù)研究中心 武漢 430063
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中國遠洋海運集團院士工作站 上海 200135
基金項目: 國家自然科學(xué)基金 (52261160383, 52022073, 62073251), 裝備預研教育部聯(lián)合基金 (8091B022239), 海南自然科學(xué)基金創(chuàng )新研究團隊項目 (722CXTD518), 武漢基礎研究知識創(chuàng )新計劃 (2022010801010181), 舟山科技局項目 (2022C41006), 武漢理工大學(xué)重慶研究院研究項目 (YF2021-12) 資助
Adaptive Neural Output Feedback Trajectory Tracking Control for USVs Under False-data-injection Attacks
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School of Naval Architecture and Maritime, Zhejiang Ocean University, Zhoushan 316022
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State Key Laboratory of Maritime Technology and Safety, Wuhan University of Technology, Wuhan 430063
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School of Navigation, Wuhan University of Technology, Wuhan 430063
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National Engineering Research Center for Water Transport Safety, Wuhan University of Technology, Wuhan 430063
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Academician Workstation of COSCO SHIPPING Group, Shanghai 200135
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)
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摘要: 本文主要研究網(wǎng)絡(luò )環(huán)境下無(wú)人水面船舶 (Unmanned surface vessels, USVs) 遭受虛假數據注入式 (False-data-injection, FDI) 攻擊的跟蹤控制問(wèn)題. 其中, 內部和外部不確定以及輸入飽和約束等實(shí)際因素均考慮在設計中. 在控制設計過(guò)程中, 為避免將船舶速度的攻擊信號引入閉環(huán)系統, 采用分類(lèi)重構思想, 構造一種新的神經(jīng)網(wǎng)絡(luò ) (Neural network, NN) 狀態(tài)觀(guān)測器, 同時(shí)重構船舶速度和攻擊信號. 進(jìn)一步, 在backstepping 設計框架下, 利用重構的攻擊信號補償USVs 運動(dòng)學(xué)通道因虛假數據注入式攻擊引起的非匹配不確定項. 在動(dòng)力學(xué)設計通道中, 利用自適應神經(jīng)技術(shù)和單參數學(xué)習法, 重構由內部和外部不確定組成的復合不確定部分, 進(jìn)而提出自適應神經(jīng)輸出反饋控制方案. 理論分析表明, 即便在FDI 攻擊、內外不確定以及執行器飽和約束的情況下, 所提控制方案仍能迫使USVs 跟蹤給定的參考軌跡. 同時(shí), 仿真和比較結果證實(shí)了所提控制方案的有效性和優(yōu)越性.
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關(guān)鍵詞:
- 無(wú)人水面船舶 /
- 虛假數據注入式攻擊 /
- 跟蹤控制 /
- 單參數學(xué)習法 /
- 自適應神經(jīng)控制 /
- 輸出反饋
Abstract: This paper investigates the tracking control issue of unmanned surface vessels (USVs) under the attack of false-data-injection (FDI) in the network environment, and these actual factors such as internal and external uncertainties and input saturation constraints are also considered in the design. In the control design, to avoid FDI attack signals from the velocity channel being introduced into the closed-loop system, the idea of classification reconstruction is developed. Based on this idea, a novel neural network (NN) state observer is constructed to reconstruct vessels velocity and FDI attack signals. Furthermore, under the backstepping design framework, utilizing the reconstructed attack signals to compensate the mismatched uncertainties in USVs kinematic channel, which is caused by false-data-injection attacks. In the dynamic design channel, adaptive neural technology and single parameter learning method are used to reconstruct the lumped uncertain parts, which consist of internal and external uncertainties, and then the adaptive neural output feedback control scheme is proposed. The theoretical analysis shows that the proposed control scheme can make USVs track a given reference trajectory, even in the presence of FDI attacks, internal and external uncertainties, and actuator saturation constraints. At the same time, the simulation and comparison results illustrate the effectiveness and superiority of the proposed control scheme. -
圖 3 軌跡誤差${{\boldsymbol{S}}}_1$對比圖
Fig. 3 Comparison chart of trajectory errors ${{\boldsymbol{S}}}_1$
圖 4 控制輸入${\boldsymbol {\tau}}$對比圖
Fig. 4 Comparison chart of control inputs${\boldsymbol {\tau}}$
圖 5 參數估計值$\hat{{\boldsymbol{\vartheta}}}$
Fig. 5 Parameter estimation values$\hat{{\boldsymbol{\vartheta}}}$
圖 6 速度估計誤差$\tilde{{{\boldsymbol{v}}}}$對比
Fig. 6 Comparison of velocity estimation errors$\tilde{{{\boldsymbol{v}}}}$
圖 7 位置估計誤差$\tilde{{\boldsymbol{\eta}}}$對比
Fig. 7 Comparison of position estimation errors$\tilde{{\boldsymbol{\eta}}}$
圖 8 有無(wú)${\boldsymbol{\sigma}}$時(shí)${{\boldsymbol{S}}}_1$對比
Fig. 8 Comparison of${{\boldsymbol{S}}}_1$with or without${\boldsymbol{\sigma}}$
圖 9 有無(wú)${\boldsymbol{\sigma}}$時(shí)$\hat{{{\boldsymbol{v}}}}$對比
Fig. 9 Comparison of$\hat{{{\boldsymbol{v}}}}$with or without${\boldsymbol{\sigma}}$
表 1 設計參數及初始值
Table 1 Design parameters and initial values
指標 項式 數值 觀(guān)測器 $ 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} 環(huán)境擾動(dòng) $ {\boldsymbol{\wp}} $ diag{?2, ?2, ?2} $ {\boldsymbol{\Upsilon}} $ 2 × [1.5, 1.5, 1.0]T 輸入飽和限制 $ {\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 
下載: 導出CSV
表 2 不同攻擊下的控制性能對比
Table 2 Comparison of control performance under different attacks
指標 項式 未攻擊 1 倍攻擊 4 倍攻擊 8 倍攻擊 $\int_{0}^{t}\frac{\tau_i(t_f)}{t_f\;+\;0.001}{\rm d}t_f$ $ \tau_1 $ 1.524 1.522 1.535 1.605 $ \tau_2 $ 1.245 1.270 1.428 1.742 $ \tau_3 $ 0.476 0.477 0.484 0.495 $ \int_{0}^{t}|S_{1,i}|{\rm d}t_f $ $ S_{1,1} $ 3.334 3.263 3.097 2.986 $ S_{1,2} $ 3.333 3.302 3.235 3.191 $ S_{1,3} $ 0.412 0.412 0.413 0.412 $\int_{0}^{t}| \tilde{v}_i|{\rm d}t_f$ $ \tilde{v}_1 $ 0.072 0.758 3.052 6.108 $ \tilde{v}_2 $ 0.074 0.632 2.519 5.065 $ \tilde{v}_3 $ 0.008 0.018 0.093 0.193
下載: 導出CSV
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