Event-Based Resilient Formation Control of Multiagent Systems

Zhang, Dandan; Tang, Yang; Ding, Zhengtao; Qian, Feng

doi:10.1109/tcyb.2019.2910614

Cited by 23 publications

(4 citation statements)

References 52 publications

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“…Remark Along with induced theoretical findings using consensus configuration (1), the merits disclosed in this article are fully summarized below:Compared with the usual consensus setup advocated by, 7,13,42 we here account for a more practical scenario wherein interacting individuals are permitted to possess more functionalities, in contrast to the friendly message change among them, while do not incur more extra constraints such as signed graph or structurally balanced theory, and so forth; As with the bipartite consensus problem formulated by, 22,26 we here admit participating agents share more complex aggregation behaviors, apart from merely the bipartite consensus. More importantly, the idea of consensus protocol devised in this article on cooperative‐competitive information allows a clear description on characterizing interacting mechanism of the agents and the interpretation of cooperative and competitive phenomena which have been verified to a crucial factor when studying the consensus issues, regardless of whether it's for natural systems or real engineering systems; Note that Ref 43 .…”

Section: Consensus Of Cooperative‐competitive Multi‐agent Systemsmentioning

confidence: 93%

“…1. Compared with the usual consensus setup advocated by, 7,13,42 we here account for a more practical scenario wherein interacting individuals are permitted to possess more functionalities, in contrast to the friendly message change among them, while do not incur more extra constraints such as signed graph or structurally balanced theory, and so forth; 2. As with the bipartite consensus problem formulated by, 22,26 we here admit participating agents share more complex aggregation behaviors, apart from merely the bipartite consensus.…”

Section: Corollarymentioning

confidence: 99%

See 1 more Smart Citation

Consensus for cooperative–competitive network systems: An impulsive perspective

Zhang

Liu

Lou

et al. 2023

Intl J Robust & Nonlinear

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Usually, hypotheses involving cooperative interactions and continuous interplay among interacting agents may result in somewhat conservatism, drastically hurdling potential about interpretations of prevalent aggregation phenomena arising in both nature and practical application. Of interest uncovered in this article is the cooperative-competitive consensus problems for multi-agent systems by leverage of impulsive-based control perspective. We first discuss the possibility of quantifying interaction relation of the agents and the description of cooperative-competitive phenomena that capture massive interest recently.Specifically, the former is done by algebraic graph theory, following the same research avenue as the convention; while the cooperative-competitive information is described by some nonzero scaling parameter. We shall pursue the tie that enables to anchor the consensus of participating agents, without depending on the signed graph theory. Subsequently, a manner relying on the local information is carried out to induce the consensus error to circumvent the global information. Then, consensus condition, in connection with the features of both continuous and impulsivebased dynamics, is obtained, as well as several typical circumstances are also elaborated. Both the proposed consensus algorithms and the derived results are underpinned via a numerical study eventually.

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Section: Consensus Of Cooperative‐competitive Multi‐agent Systemsmentioning

confidence: 93%

Section: Corollarymentioning

confidence: 99%

Consensus for cooperative–competitive network systems: An impulsive perspective

Zhang

Liu

Lou

et al. 2023

Intl J Robust & Nonlinear

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“…In recent years, the coordination control of MASs has attracted considerable attention for their broad applications in many fields [ 1 , 2 ], such as formation control, cooperative attack, and attitude alignment. The MAS consists of a series of agents, which can communicate and interact with each other to realize multiple missions and adapt to the complex environment [ 3 , 4 ]. In particular, much attention has been paid to the problem of consensus of MASs because of their great potential applications in both economic and military.…”

Section: Introductionmentioning

confidence: 99%

Intelligent L2-L∞ Consensus of Multiagent Systems under Switching Topologies via Fuzzy Deep Q Learning

Cheng

Song

et al. 2022

Computational Intelligence and Neuroscience

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The problem of intelligent L2-L∞ consensus design for leader-followers multiagent systems (MASs) under switching topologies is investigated based on switched control theory and fuzzy deep Q learning. It is supposed that the communication topologies are time-varying, and the model of MASs under switching topologies is constructed based on switched systems. By employing linear transformation, the problem of consensus of MASs is converted into the issue of L2-L∞ control. The consensus protocol is composed of the dynamics-based protocol and learning-based protocol, where the robust control theory and deep Q learning are applied for the two parts to guarantee the prescribed performance and improve the transient performance. The multiple Lyapunov function (MLF) method and mode-dependent average dwell time (MDADT) method are combined to give the scheduling interval, which ensures stability and prescribed attenuation performance. The sufficient existing conditions of consensus protocol are given, and the solutions of the dynamics-based protocol are derived based on linear matrix inequalities (LMIs). Then, the online design of the learning-based protocol is formulated as a Markov decision process, where the fuzzy deep Q learning is utilized to compensate for the uncertainties and achieve optimal performance. The variation of the learning-based protocol is modeled as the external compensation on the dynamics-based protocol. Therefore, the convergence of the proposed protocol can be guaranteed by employing the nonfragile control theory. In the end, a numerical example is given to validate the effectiveness and superiority of the proposed method.

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“…Formation of multiagent systems subject to communication failures is discussed in [24]. To overcome the deception attacks, a distributed resilient control method is designed to cope with the formation problem of multiagent systems [25].…”

mentioning

confidence: 99%

Adaptive Learning Gain based Resilient Control of Multiagent Systems with Partial Uncertain Control Coefficients Under FDI Attacks

Yang,

Long,

Lei

2023

Preprint

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This paper presents a novel approach to security resilient control in collective multiagent systems, incorporating an adaptive learning gain mechanism. The proposed method addresses challenges posed by bounded external disturbances, partial false-data-injection (FDI) attacks on the actuators, and partial unknown time-varying control coefficients. The presented synthesized control consists of following parts. Specifically, a robust adaptive control law with implicit learning is proposed to address the modeling parameterized uncertainties and disturbances. An adaptive learning gain-based control technique is designed to reduce the adversarial effect caused by mismatched disturbances with time-varying parameters, which curbs the cooperative errors to the assigned field. A resilient control approach is established to mitigate the detrimental impact of malicious attacks on actuators. An adaptive control strategy, utilizing a novel congelation of variables, is implemented to address the control issue pertaining to partially unknown control coefficients. This technique ensures that control inputs align with the practical actuators by establishing feasible control bounds. The property of the resulting closed-loop error multiagent systems is analyzed. Finally, the efficacy of the designed controller is illustrated by providing an illustrative example.

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Event-Based Resilient Formation Control of Multiagent Systems

Cited by 23 publications

References 52 publications

Consensus for cooperative–competitive network systems: An impulsive perspective

Consensus for cooperative–competitive network systems: An impulsive perspective

Intelligent L2-L∞ Consensus of Multiagent Systems under Switching Topologies via Fuzzy Deep Q Learning

Adaptive Learning Gain based Resilient Control of Multiagent Systems with Partial Uncertain Control Coefficients Under FDI Attacks

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