Sparse Linear Injection Attack on Multi-Agent Consensus Control Systems

Tsang, Kam Fai Elvis; Huang, Mengyu; Johansson, Karl Henrik; Shi, Ling

doi:10.1109/lcsys.2020.3004920

Cited by 23 publications

(10 citation statements)

References 18 publications

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“…i (δ i ) 3: Evaluate the confidence of each triangulation set according to (7); 13: Reduce according to (11). 14: end 15: Normalize the modified weights using (12); 16: Update the coordinates of i based on the parameters obtained above according to (10).…”

Section: Attacksmentioning

confidence: 99%

“…Deception attacks, as one of the most typical cyber attacks, may ruin the integrity of data by tampering the information transmitted through communication networks, and eventually lead to undesirable consequences such as malicious manipulation or even system breakdown [10]. To alleviate the negative influence resulted from deception attacks, a number of secure control schemes have been studied [11]- [15]. For example, by designing a pinning strategy-based impulsive controller, the problem of synchronization under deception attacks in multi-agent systems was investigated in [11].…”

mentioning

confidence: 99%

“…To alleviate the negative influence resulted from deception attacks, a number of secure control schemes have been studied [11]- [15]. For example, by designing a pinning strategy-based impulsive controller, the problem of synchronization under deception attacks in multi-agent systems was investigated in [11]. A consensus protocol of multiagent systems under sparse linear injection attacks was designed and the relevant optimization problem was solved efficiently by the means of alternating direction method of multipliers [12].…”

mentioning

confidence: 99%

See 2 more Smart Citations

A Trust Assessment-Based Distributed Localization Algorithm for Sensor Networks Under Deception Attacks

Wang

Chen

Shi

et al. 2022

IEEE/CAA J. Autom. Sinica

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Section: Attacksmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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A Trust Assessment-Based Distributed Localization Algorithm for Sensor Networks Under Deception Attacks

Wang

Chen

Shi

et al. 2022

IEEE/CAA J. Autom. Sinica

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“…For a general overview, the readers are directed to References 8‐10. There are various issues in the design and implementation of consensus control including, time‐delay, 11 uncertainties, 12 attacks/packet losses, 13,14 link failures, 15 intermittent communication 16 and so on. Starting from the Viscek model, 17 there has been a surge in the research work to address such problems.…”

Section: Introductionmentioning

confidence: 99%

Robust consensus of uncertain multi agent systems with one‐sided Lipschitz nonlinearity

Ramachandran

Juang

2022

Intl J Robust & Nonlinear

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This article develops protocols for the consensus of an uncertain nonlinear multi-agent system that satisfies the one-sided Lipschitz and quadratic-inner bounded conditions. Fully distributed consensus protocols with observer-based and observer-less schemes are investigated. For the observer-based system, a full order Luenberger observer is designed. To overcome the difficulties of designing the controller and observer-based controller gains, sufficient conditions are provided with a complete linear matrix inequality characterization. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed approach.

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“…The consensus problem [1,2], where a group of agents in the same network collectively seek to reach a mutually agreeable state, is not robust to nonlinearity, such as malicious agents or external disturbances [3,4]. To overcome this, Rehan et al…”

Section: Introductionmentioning

confidence: 99%

Distributed Event-Triggered Learning-Based Control for Nonlinear Multi-Agent Systems

Tsang

Johansson

2021

2021 60th IEEE Conference on Decision and Control (CDC)

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This paper studies event-triggered consensus control for heterogenous nonlinear multi-agent systems. We present a new distributed nonlinear event-triggered control algorithm integrating basic radial basis function neural network with event-based control. We show that it can handle any unknown dynamics linear in the control input, achieving practical consensus without Zeno behaviour. A numerical example is provided to highlight the effectiveness of the proposed algorithm in terms of learning the unknown nonlinear dynamics. I. INTRODUCTIONThe consensus problem [1,2], where a group of agents in the same network collectively seek to reach a mutually agreeable state, is not robust to nonlinearity, such as malicious agents or external disturbances [3,4]. To overcome this, Rehan et al.[5] presented a modified consensus control protocol under one-sided Lipchitz nonlinearity. Ma et al.[6] adopted a feedback controller to handle stochastic nonlinear disturbances. These algorithms, however, require at least partial, if not full, information about the nonlinearity, such as Lipchitz constants and boundedness. It could be a strong assumption in physical systems as we often do not have sufficient knowledge or exact model of the dynamics. This motivates the use of learning techniques, specifically neural networks, to compensate the influence of the nonlinearity which has shown promising capabilities of estimating unknown dynamics [7][8][9][10][11]. In particular, Zhang et al. [12] proposed a continuous-time consensus algorithm with radial basis function neural network as an estimator of the unknown dynamics.The learning-based consensus algorithms, similar to classical consensus algorithms [13], require continuous information exchange between the neighbours, which is often impractical in reality. In view of this, self-triggered and event-triggered control were introduced to mitigate such connection in control systems in general [14,15]. Eventtriggered consensus control problems have also been widely investigated [16][17][18][19][20]. Yi et al. [21] proposed a dynamic event-triggering law that drastically reduces communication amongst agents without the need for a heavy computation overhead. Tsang et al. [20] designed a stochastic trigger based on existing deterministic counterparts to achieve even lower communication rate. Exponential convegence to consensus can be achieved and require much less communication than time-triggered approaches.

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Sparse Linear Injection Attack on Multi-Agent Consensus Control Systems

Cited by 23 publications

References 18 publications

A Trust Assessment-Based Distributed Localization Algorithm for Sensor Networks Under Deception Attacks

A Trust Assessment-Based Distributed Localization Algorithm for Sensor Networks Under Deception Attacks

Robust consensus of uncertain multi agent systems with one‐sided Lipschitz nonlinearity

Distributed Event-Triggered Learning-Based Control for Nonlinear Multi-Agent Systems

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