Deception attacks on event-triggered distributed consensus estimation for nonlinear systems

Ren, Hongru; Cheng, Zhijian; Qin, Jiahu; Lu, Renquan

doi:10.1016/j.automatica.2023.111100

Cited by 114 publications

(12 citation statements)

References 30 publications

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“…Besides, by (30), we obtain that q(t) = 0, η(t) = A η(t) and Ψ(t) = −k 2 ̇Ψ(t) are bounded. In addition, by (27), the boundedness of q (3) s (t) = Ψ𝜂 + 2 ̇Ψ η + Ψ η − 𝛼 0 ( q − q) is guaranteed. As a conclusion, Λ(q(t), q(t), qs (t), qs (t)) is bounded.…”

Section: Resultsmentioning

confidence: 97%

Sampled‐data robust practical tracking of Euler‐Lagrange systems with an uncertain exosystem

Zeng,

Liu

2023

Intl J Robust & Nonlinear

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This article focuses on the problem of sampled‐data practical tracking for Euler‐Lagrange systems subject to uncertain parameters. We assume that the system matrix of the exosystem and the Euler‐Lagrange system both contain unknown parameters, which is clearly more practical. The existence of unknown system parameters invalidates existing control strategies and poses a major challenge to the solvability of the problem. With the help of the internal model principle and the adaptive control technology, we propose a novel sampled‐data dynamic compensator to overcome the challenge of unknown parameters in exosystems. In particular, a virtual sampling adaptive control input is proposed to deal with uncertainties in the exosystem matrix. Then, a sampled‐data control law is constructed to guarantee that, by any fast predefined rate, the tracking error exponentially approaches any small predefined ranges of the origin, thus ensure the tracking performance. Finally, we apply our result to a three‐link robot manipulator system.

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Section: Resultsmentioning

confidence: 97%

Sampled‐data robust practical tracking of Euler‐Lagrange systems with an uncertain exosystem

Zeng,

Liu

2023

Intl J Robust & Nonlinear

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“…With the further development of artificial intelligence, deep learning models have received extensive attention and produced a series of excellent results, such as convolution neural network (CNN), recurrent neural network (RNN), long‐short term memory (LSTM), bidirectional long‐short term memory (BiLSTM), time convolution network. Because of the better feature learning ability and big data learning ability, neural networks are widely used in the field of PV power forecasting [16–23].…”

Section: Introductionmentioning

confidence: 99%

“…Among them, LSTM and BiLSTM, as variants of RNN, are promising methods to improve the long‐range dependence of RNN. The authors considered an independent day‐ahead PV power forecasting model based on long‐short‐term memory recurrent neural network and time correlation principles, and the results indicated that the performance of the forecasting model can be further improved [18]. The authors proposed a new deep learning BiLSTM algorithm for large‐scale PV plants and proved that the time series forecasting was only reliable for 1 h ahead prediction [22].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Deep Learning Model for Photovoltaic Power Forecasting Based on Feature Extraction and BiLSTM

Lin,

Zhang,

2024

IEEJ Transactions Elec Engng

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In this paper, a hybrid photovoltaic power forecasting model is proposed based on bidirectional long‐short‐term memory network. Firstly, the photovoltaic power and meteorological data are decomposed by ensemble empirical mode decomposition. Secondly, key features in the meteorological subsequences are extracted by kernel principal component analysis method to eliminate the correlation and redundancy in the original meteorological sequence and reduce the input dimension of the model, then the coupling time characteristics between meteorological subsequences and photovoltaic power subsequence are further explored. Thirdly, a bidirectional long‐short‐term memory network optimized by improved particle swarm optimization based on inertia weight of anti‐sine function is proposed, and then the eventual photovoltaic power forecasting results are obtained by superimposing the predicted values of each prediction component. The experiment is carried out with the operation data of the Inner Mongolia power grid, and the results show that the proposed forecasting model exhibits higher prediction accuracy. © 2023 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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“…25 Another novel distributed control scheme for uncertain nonlinear MASs under DoS attacks was proposed in Reference 26. As the research on MASs under DoS attacks gradually deepens, the solutions to mitigate the negative effects of DoS attacks have been further developed. The event-triggered (ET) mechanisms [27][28][29][30][31][32][33] have gained considerable attention from numerous scholars because of its advantage of saving communication resources. In Reference 27, a dynamic ET control strategy was proposed to accomplish consensus control goals for MASs under DoS attacks.…”

Section: Introductionmentioning

confidence: 99%

“…This method dynamically adjusts ET threshold conditions based on the real-time results generated by the designed detection mechanism to ensure the normal transmission of control signals. Compared with some existing works, [27][28][29][30][31][32] the proposed method has the advantage of timeliness and accuracy due to the correlation between detection mechanism and ET threshold conditions. 3.…”

Section: Introductionmentioning

confidence: 99%

Detection‐based compound event‐triggered secure control for nonlinear multiagent systems

Zhu,

Cao,

Ren

et al. 2024

Intl J Robust & Nonlinear

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SummaryThis paper designs a distributed dynamic‐detection‐based event‐triggered control method to address the secure tracking control problem. A distributed online iterative dynamic detection mechanism is designed to detect denial‐of‐service (DoS) attacks. The detection results are transmitted into a proposed ET method to adjust the triggering threshold, which can mitigate the impact of DoS attacks. Moreover, the signals of state, observations of disturbance observers and adaptive laws are calculated to generate the trigger signals in the control signal simultaneously, which can help conserve communication resources and reduce the occurrence of pulse phenomenon. Meanwhile, since the predictor exists certain prediction errors that will reduce the tracking performance, a speed function is introduced to ensure the achievement of the control objective. The designed solution guarantees semiglobally uniformly ultimately bounded for all signals within the closed‐loop system. Finally, the effectiveness of control scheme is demonstrated through several simulation results.

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Deception attacks on event-triggered distributed consensus estimation for nonlinear systems

Cited by 114 publications

References 30 publications

Sampled‐data robust practical tracking of Euler‐Lagrange systems with an uncertain exosystem

Sampled‐data robust practical tracking of Euler‐Lagrange systems with an uncertain exosystem

A Novel Hybrid Deep Learning Model for Photovoltaic Power Forecasting Based on Feature Extraction and BiLSTM

Detection‐based compound event‐triggered secure control for nonlinear multiagent systems

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