Adaptive Guaranteed-Performance Consensus Control for Multi-Agent Systems with an Adjustable Convergence Speed

Ma, Haiying; Jia, Xiaoxu; Cai, Ning; Xi, Jianxiang

doi:10.1155/2019/5190301

Cited by 15 publications

(10 citation statements)

References 41 publications

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“…Recently, the distributed cooperative control of multiagent systems has aroused extensive attentions in multiple fields, including wireless communication, robotics, and distributed computation as shown in References . As an important research topic on the distributed control, formation control refers to design a control strategy with the neighboring information such that a group of autonomous agents reach an expected geometrical shape.…”

Section: Introductionmentioning

confidence: 99%

Robust time‐varying formation design for multiagent systems with disturbances: Extended‐state‐observer method

Wang

et al. 2020

Intl J Robust & Nonlinear

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102

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Robust time-varying formation design problems for second-order multiagent systems subjected to external disturbances are investigated. First, by constructing an extended state observer, the disturbance compensation is estimated, which is a critical term in the proposed robust time-varying formation control protocol. Then, an explicit expression of the formation center function is determined and impacts of disturbance compensations on the formation center function are presented. With the formation feasibility conditions, robust time-varying formation design criteria are derived to determine the gain matrix of the formation control protocol by utilizing the algebraic Riccati equation technique. Furthermore, the tracking performance and the robustness property of multiagent systems are analyzed. Finally, the numerical simulation is provided to illustrate the effectiveness of theoretical results. K E Y W O R D S algebraic Riccati equation, extended state observer, external disturbance, multiagent system, robust time-varying formation 2796

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Section: Introductionmentioning

confidence: 99%

Robust time‐varying formation design for multiagent systems with disturbances: Extended‐state‐observer method

Wang

et al. 2020

Intl J Robust & Nonlinear

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102

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“…us, our model only considers some basic social factors, with the idiosyncrasy of individuals embodied by random noises. To the best of our knowledge, most of the parallel social systems are composed of individuals and communities, which are agent-based and networked [27][28][29][30][31][32], with some of the analytical properties potentially acquirable by relevant theories in system science [26,33]. It is worth nothing that the primary goal of virtual simulation systems is not for mimicking the real-world counterparts quantitatively, instead, they should be very helpful for verifying, interpreting, and enlightening the underlying factors and the possibility of some of the phenomena and mechanisms, qualitatively.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation Analysis of Journal Impact Factor Dynamics Based on Submission and Citation Rules

et al. 2020

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The variation of the journal impact factor is affected by many statistical and sociological factors such as the size of citation window and subject difference. In this work, we develop an impact factor dynamics model based on the parallel system, which can be used to analyze the correlation between the impact factor and certain elements. The parallel model aims to simulate the submission and citation behaviors of the papers in journals belonging to a similar subject, in a distributed manner. We perform Monte Carlo simulations to show how the model parameters influence the impact factor dynamics. Through extensive simulations, we reveal the important role that certain statistics elements and behaviors play to affect impact factors. The experimental results and analysis on actual data demonstrate that the value of the JIF is comprehensively influenced by the average review time, average number of references, and aging distribution of citation.

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“…There have been a huge number of research results to date on the control problems of vehicles due to its widespread applications in robotics, aircraft, vessels, and so forth [1]- [5]. As the demand for practical applications grows, vehicles usually need to carry out some tasks cooperatively as a multi-agent systems [6]- [8]. Particularly, in both military and civilian areas, vehicles are often sent on monitoring or protecting tasks with circular formation around targets, and this is usually called the rotating encirclement problem and can be regarded as a special consensus problem of multi-agent systems [9].…”

Section: Introductionmentioning

confidence: 99%

Distributed Multi-Target Rotating Encirclement Control of Second-Order Multi-Agent Systems With Nonconvex Input Constraints

Zhang

Ling

2020

IEEE Access

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This paper investigates the collective multi-target rotating encirclement formation problem of second-order multi-agent systems, where the inputs are constrained in nonconvex sets. The objective requires that all agents rotate around targets' geometric center with the desired radius and angular velocity. Firstly, the targets' geometric center and rotating radius are calculated by two distributed fixed-time estimators based on the neighbors' information. Then, with the complex domain theory, we construct two consensus vectors complying with the conditions of the multi-target rotating encirclement formation, and propose a distributed multi-target rotating encirclement control scheme to force all agents achieve the desired formation structure. Moreover, a sufficient condition is provided for choosing design parameters with the aid of Lyapunov theory. In order to handle the problem caused by the nonconvex input constraints, a constraint operator is introduced to ensure all control inputs always lie in the corresponding nonconvex sets. Finally, numerical simulation results are presented to demonstrate the effectiveness and correctness of our theoretical control scheme. INDEX TERMS Rotating encirclement control, nonconvex input constraints, distributed estimator, second-order multi-agent systems.

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Adaptive Guaranteed-Performance Consensus Control for Multi-Agent Systems with an Adjustable Convergence Speed

Cited by 15 publications

References 41 publications

Robust time‐varying formation design for multiagent systems with disturbances: Extended‐state‐observer method

Robust time‐varying formation design for multiagent systems with disturbances: Extended‐state‐observer method

Modeling and Simulation Analysis of Journal Impact Factor Dynamics Based on Submission and Citation Rules

Distributed Multi-Target Rotating Encirclement Control of Second-Order Multi-Agent Systems With Nonconvex Input Constraints

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