Time‐varying formation control of linear multiagent systems with time delays and multiplicative noises

Jia, Ruru; Zong, Xiaofeng

doi:10.1002/rnc.5754

Cited by 17 publications

(3 citation statements)

References 39 publications

(63 reference statements)

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“…Based on these results, the containment control problem was studied in [15], the bipartite consensus problem was concerned in [16] and the leader‐following consensus of MASs was showed in [17, 18]. For multiplicative noise, its intensity depends on states of the system, for example, measuring relative states through analogue fading channels [19]. For leaderless consensus, Li et al.…”

Section: Introductionmentioning

confidence: 99%

Dynamic event‐triggered leader‐following consensus of nonlinear multi‐agent systems with measurement noises

Chen

Yan

Ren

et al. 2023

IET Control Theory & Appl

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This paper investigates a leader-following consensus problem for nonlinear multi-agent systems (MASs) with measurement noises under fixed and Markovian switching topologies, respectively. Noises are considered when each agent measures the states of its neighbours, where intensities of noises are vector functions of relative states. To alleviate the utilization of communication and computation resources, a dynamic event-triggered consensus protocol is designed, where the coupling strength is restricted in a given interval. By using the stochastic stability theorem, it is shown that leader-following consensus is achieved under fixed topology, where an estimation of the convergence rate is given. Moreover, the problem is studied under switching topologies subjecting to the Markovian process, which is applicable to practical situations with a time-varying communication environment. Finally, simulation examples are given to show the correctness of the proposed results.

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Section: Introductionmentioning

confidence: 99%

Dynamic event‐triggered leader‐following consensus of nonlinear multi‐agent systems with measurement noises

Chen

Yan

Ren

et al. 2023

IET Control Theory & Appl

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“…The utilization of adaptive control in [10][11][12] is able to overcome the uncertainty of the system model parameters by adjusting the parameters online thus enabling NMASs to adapt to the environmental changes and thus track the desired trajectory. [13] and [14] implemented time-varying formation tracking control with noise or disturbance using Lyapunov functions and adaptive neural network methods, respectively. In general, the design of control algorithms for NMASs ultimately needs to achieve state consensus or output consensus among individual agents, and hence the consensus control problem has been one of the cores of research on multi-agent systems.…”

Section: Introductionmentioning

confidence: 99%

An output formation consensus control for leader–follower networked multi‐agent systems under communication constraints and switching topologies

Lei

Zhang

Ionescu

et al. 2023

Asian Journal of Control

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In this paper, the formation consensus problem for a class of leader–follower networked multi‐agent systems under communication constraints and switching topologies is investigated. A networked predictive control scheme is proposed to achieve stability and output formation consensus with the switching topology, capable of compensating for data loss and time delays in the network. By equating the whole closed‐loop networked multi‐agent system with the proposed control scheme to the corresponding switched system, the sufficient and necessary condition of output formation consensus and stability for agents is given. Finally, using three‐degree‐of‐freedom air‐bearing spacecraft simulators as the control objects, the proposed scheme is demonstrated to be able to actively compensate for the communication constraints through numerical simulations, and it is also verified to have a good control performance by further realizing the formation task of the simulators through practical experiments.

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“…There are many studies about formation control of one leader 9‐11 . Compared with a single leader, multiple leaders can improve the fault tolerance of the system.…”

Section: Introductionmentioning

confidence: 99%

Multiple leaders formation tracking with switching topology and aperiodic sampled data under intermittent control scheme

Liu

Deng

Zhao

et al. 2022

Intl J Robust & Nonlinear

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This article studies the problem of second-order formation tracking with multiple leaders under intermittent control scheme in stochastic multi-agent networked systems. Based on the aperiodic sampled data, the formation tracking control protocol is proposed by considering switching topology and the time-varying transmission delay of information interaction between each agent. Using the algebraic graph theory, stochastic systems theory, Lyapunov theory, and Halanay inequality, the sufficient conditions of mean square stability for formation tracking with multiple leaders are obtained. To further alleviate the computation resources of the controller, the intermittent control strategy is proposed. Meanwhile, a novel stability analysis method for intermittent control is introduced based on an auxiliary system and comparison principle. Furthermore, the lower bound of the duty cycle for a certain control period is obtained and the desired formation mode is also reached under the designed intermittent control scheme. Finally, numerical examples are given to verify the correctness and effectiveness of the proposed methods.

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Time‐varying formation control of linear multiagent systems with time delays and multiplicative noises

Cited by 17 publications

References 39 publications

Dynamic event‐triggered leader‐following consensus of nonlinear multi‐agent systems with measurement noises

Dynamic event‐triggered leader‐following consensus of nonlinear multi‐agent systems with measurement noises

An output formation consensus control for leader–follower networked multi‐agent systems under communication constraints and switching topologies

Multiple leaders formation tracking with switching topology and aperiodic sampled data under intermittent control scheme

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