Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs

Yazıcıoğlu, Yasin; Egerstedt, Magnus; Shamma, Jeff S.

doi:10.1109/tnse.2015.2503983

Cited by 42 publications

(24 citation statements)

References 45 publications

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“…In [45], a decentralized graph reconstruction scheme is presented to build a powerful MAS. A distributed non-periodic predictive control method based on the graph theory for MAS is designed in [46.…”

Section: A Modeling Methods For Distributed Multi-agent Systemmentioning

confidence: 99%

“…Graph theoretic topology model [45][46][47][48][49][50][51] ·Simple model structure ·High redundancy and easy to expand ·Robustness is greatly affected by graph Non-cooperative dynamic game model [52][53][54] ·Each agent can achieve the optimal balanced state ·Algorithm is complex and time-consuming Genetic algorithm [55][56][57] ·High prediction accuracy ·Fast convergence ·Scalability and parallelism operation ·Most of the parameters depend on experience ·Slow dynamic response PSO algorithm [58][59][60][61] ·Simple model structure ·Fast computation speed ·Efficient economic scheduling ·Improve the frequency and voltage of MG ·Not handling the discrete optimization problems…”

Section: Merits Drawbacksmentioning

confidence: 99%

“…Distributed multi-agent control method has been widely used to establish optimal model to enhance reliability and energy management, optimization, and improve the performance of ancillary services [44]. Various methods for system modelling, including topology models and mathematic models, in MAS have been reviewed in this paper, such as the graph topology model [45][46][47][48][49][50][51], non-cooperative game model [52][53][54], genetic algorithm [55][56][57] and particle swarm optimization (PSO) [58][59][60][61], etc. Moreover, consensus protocol is the rule of interaction among multiple agents in the complex systems, which describes the process of information interaction among agents and their neighbors.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

366

182

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Abstract-The increasing integration of the distributed renewable energy sources highlights the requirement to design various control strategies for microgrids (MGs) and microgrid clusters (MGCs). The multi-agent system (MAS)-based distributed coordinated control strategies shows the benefits to balance the power and energy, stabilize voltage and frequency, achieve economic and coordinated operation among the MGs and MGCs. However, the complex and diverse combinations of distributed generations in multi-agent system increase the complexity of system control and operation. In order to design the optimized configuration and control strategy using MAS, the topology models and mathematic models such as the graph topology model, non-cooperative game model, the genetic algorithm and particle swarm optimization algorithm are summarized. The merits and drawbacks of these control methods are compared. Moreover, since the consensus is a vital problem in the complex dynamical systems, the distributed MAS-based consensus protocols are systematically reviewed. NOMENCLATURE

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Section: A Modeling Methods For Distributed Multi-agent Systemmentioning

confidence: 99%

Section: Merits Drawbacksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Han

Zhang

et al. 2018

IEEE Trans. Power Electron.

366

182

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“…We provide some discussion about this aspect in the Appendix of this paper. For further details on this subject and a distributed n = 20 n = 40 n = 60 algorithm for building random regular graphs via local graph transformations, we refer the interested readers to [11] and the references therein.…”

Section: F Structural Robustness Of Random Regular Graphsmentioning

confidence: 99%

“…This question has motivated many studies on the robustness of consensus networks. Graph measures such as connectivity (e.g., [8], [9]), expansion ratios (e.g., [10], [11]), Kirchoff index (e.g., [2], [3], [12]), and centrality (e.g., [13], [14]) have been used in the literature to quantify the robustness to different types of disturbances. This paper is focused on the robustness of undirected consensus networks to noisy interactions.…”

Section: Introductionmentioning

confidence: 99%

Structural Robustness to Noise in Consensus Networks: Impact of Average Degrees and Average Distances

Yazıcıoğlu

Abbas

Shabbir

2019

2019 IEEE 58th Conference on Decision and Control (CDC)

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We investigate how the graph topology influences the robustness to noise in undirected linear consensus networks. We measure the structural robustness by using the smallest possible value of steady state population variance of states under the noisy consensus dynamics with edge weights from the unit interval. We derive tight upper and lower bounds on the structural robustness of networks based on the average distance between nodes and the average node degree. Using the proposed bounds, we characterize the networks with different types of robustness scaling under increasing size. Furthermore, we show that there is a fundamental trade-off between the structural robustness and the average degree of networks. We then show that, random k-regular graphs (the degree of each node is k) with n nodes typically have near optimal structural robustness among all the graphs with size n and average degree k. We also show that when k increases properly with n, random k-regular graphs maintain a structural robustness within a constant factor of the best possible value (corresponds to the complete graph) while also having the minimum average degree required for such robustness.

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Event‐triggered finite‐time practical consensus of multiagent systems with general directed communication graphs

Wan

Huang

2020

Intl J Robust & Nonlinear

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In this article, an event-triggered sampled-data implementation of a class of finite-time consensus controllers is considered for first-order multiagent systems under general directed communication graphs. Each agent only updates its control input at its event-triggering time instants. From any initial condition of the agents, it is rigorously proved that the proposed control strategy can achieve consensus with arbitrarily small errors in a finite time. Zeno behavior is also proven to be excluded. Moreover, a self-triggered sampled-data implementation is proposed to further reduce the communication requirement. Both leaderless and leader-following consensus are considered. Generalizations to event-triggered consensus of second-order multiagent systems are then presented. Simulations of the frequency and voltage control of smart grids are finally given to illustrate the effectiveness of the proposed protocols. K E Y W O R D S directed graph, event-triggered consensus, finite-time control, multiagent systems 1 INTRODUCTION Along with the rapid development of sensing, communication, and computation technology, the coordination control of multiagent systems has attracted more and more attention due to its widespread applications in areas such as multiple robots systems, smart grids networks, and synchronizing chaotic circuits. 1-3 Consensus control has received particular consideration from various researchers due to its primary function in the distributed control of multiagent systems. Through interacting with only neighboring agents, the goal of consensus is to make the local variables of the agents reach common values across the network. It lays the foundation of many other control tasks such as formation, containment, and flocking. 4 Extensive results on consensus have been reported in the literature 5-8 covering different kinds of agent dynamics and various types of communication graphs. In view of the advantages of finite-time controllers such as fast convergence speed, high-control precision and robustness to disturbances, finite-time consensus controllers have been studied in the literature recently. 9-19 The authors in References 9,10 have focused on the finite-time coordination problems for first-order integrator systems. For second-order systems with undirected communication graphs, a class of continuous finite-time consensus controllers was proposed in Reference 11 based on homogeneous systems theory. By using similar techniques, the finite-time consensus problem of second-order multiagent systems subject to input saturation was investigated in Reference 12, and a connectivity-preserving finite-time consensus controller was proposed in Reference 13. In Reference 14, terminal sliding

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Formation of Robust Multi-Agent Networks through Self-Organizing Random Regular Graphs

Cited by 42 publications

References 45 publications

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

MAS-Based Distributed Coordinated Control and Optimization in Microgrid and Microgrid Clusters: A Comprehensive Overview

Structural Robustness to Noise in Consensus Networks: Impact of Average Degrees and Average Distances

Event‐triggered finite‐time practical consensus of multiagent systems with general directed communication graphs

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