Consensus of Multi-Agent Networks With Aperiodic Sampled Communication Via Impulsive Algorithms Using Position-Only Measurements

Liu, Zhi‐Wei; Guan, Zhi‐Hong; Shen, Xuemin; Feng, Gang

doi:10.1109/tac.2012.2214451

Cited by 222 publications

(80 citation statements)

References 25 publications

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“…However, the velocity states of agents are often unavailable in practice because of the technical limitations or communication constraints. Therefore consensus algorithms using only position information have been proposed recently [20][21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Consensus and performance optimisation of multi‐agent systems with position‐only information via impulsive control

Liu

Guan

2013

IET Control Theory & Applications

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In this study, the consensus problem of second-order multi-agent systems (MAS) with position-only information is studied. Allowable sampling period for which second-order consensus can be achieved is obtained with two impulsive consensus algorithms. It is shown that if there is at least one eigenvalue of the Laplcian matrix with a non-zero imaginary part, consensus cannot be achieved for sufficiently small or large impulsive periods for both algorithms. Furthermore, the convergence performance of the MAS is optimised. Convergence speed, asymptotical decay factor and per-step decay factor of the error energy are utilised to investigate the convergence performance, and the relationship among impulsive period, topology structure and convergence performance is derived. Finally, numerical examples are given to validate our theoretical results.

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Section: Introductionmentioning

confidence: 99%

Consensus and performance optimisation of multi‐agent systems with position‐only information via impulsive control

Liu

Guan

2013

IET Control Theory & Applications

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“…(18) solved by ADMM, we reformulate the proposed optimal day-ahead scheduling problem (17) as follows.…”

Section: An Overview On Admmmentioning

confidence: 99%

“…This is also called the social welfare maximization and has attracted much attention [12][13][14][15][16]. Besides, distributed coordination approaches [17,18], which are widely applied in many other fields, have been introduced into the day-ahead scheduling in a smart grid recently.…”

Section: Introductionmentioning

confidence: 99%

Distributed Optimal Day-Ahead Scheduling in a Smart Grid: A Trade-Off among Consumers, Power Suppliers, and Transmission Owners

Xiong

Liu

2017

Complexity

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To cope with the challenges due to increasing peak load, an optimal day-ahead scheduling problem for social welfare maximization is proposed, in which not only the comfort level of consumers and costs of power suppliers but also the power losses in transmission and operation costs of transmission owners are taken into account. Then this optimal day-ahead scheduling problem is reformulated and solved via the alternating direction method of multipliers (ADMM), by which fast convergence is guaranteed and the privacy of participants is ensured, in a distributed manner. Specifically, in the proposed distributed optimal day-ahead scheduling, the hourly prices for consumers are divided into hourly supply prices and hourly delivery prices, which will be updated by the independent system operator based on the hourly demand-supply situations and hourly demand-delivery situations, respectively. And the consumers, power suppliers, and transmission owners make their individual optimal day-ahead scheduling based on their individual hourly prices, hourly supply prices, and hourly delivery prices, respectively, until the hourly demand-supply balances and hourly demand-delivery balances are achieved. Effectiveness of the proposed distributed optimal day-ahead scheduling is verified by the cases studied.

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“…Compared with these continuous control methods, impulsive control is an efficient method to deal with the dynamical systems which cannot be controlled by continuous control methods [21][22][23][24][25]. In addition, in consensus processes, one node receives the information from its neighbor nodes only at the discrete time instants, which dramatically reduces the amount of synchronization information transmitted between the nodes of multi-agent systems and makes the method more efficient in a large number of real-life applications [26][27][28][29][30][31]. In the literatures dealing with the impulsive consensus problem, several important topics have been addressed, including impulsive consensus with communication delay [32][33][34], some investigations about average consensus [35,36], networks with switching topology [37], etc.…”

Section: Introductionmentioning

confidence: 99%

Impulsive consensus of multi-agent nonlinear systems with control gain error

Zhang

Zhao

et al. 2016

Neurocomputing

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Consensus of Multi-Agent Networks With Aperiodic Sampled Communication Via Impulsive Algorithms Using Position-Only Measurements

Cited by 222 publications

References 25 publications

Consensus and performance optimisation of multi‐agent systems with position‐only information via impulsive control

Consensus and performance optimisation of multi‐agent systems with position‐only information via impulsive control

Distributed Optimal Day-Ahead Scheduling in a Smart Grid: A Trade-Off among Consumers, Power Suppliers, and Transmission Owners

Impulsive consensus of multi-agent nonlinear systems with control gain error

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