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Journal articles on the topic 'Discrete Consensus'

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Author: Grafiati

Published: 11 March 2023

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1

Malinowska, Agnieszka B., Ewa Schmeidel, and Małgorzata Zdanowicz. "Discrete leader-following consensus." Mathematical Methods in the Applied Sciences 40, no. 18 (August 11, 2017): 7307–15. http://dx.doi.org/10.1002/mma.4530.

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2

Chen, Yao, and Jinhu Lü. "Delay-induced discrete-time consensus." Automatica 85 (November 2017): 356–61. http://dx.doi.org/10.1016/j.automatica.2017.07.059.

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3

Zhu, Minghui, and Sonia Martínez. "Discrete-time dynamic average consensus." Automatica 46, no. 2 (February 2010): 322–29. http://dx.doi.org/10.1016/j.automatica.2009.10.021.

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4

Montijano, Eduardo, Juan Ignacio Montijano, Carlos Sagüés, and Sonia Martínez. "Robust discrete time dynamic average consensus." Automatica 50, no. 12 (December 2014): 3131–38. http://dx.doi.org/10.1016/j.automatica.2014.10.005.

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5

Wang, Xiaoping, and Jinliang Shao. "H∞Consensus for Discrete-Time Multiagent Systems." Discrete Dynamics in Nature and Society 2015 (2015): 1–6. http://dx.doi.org/10.1155/2015/380184.

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AnH∞consensus problem of multiagent systems is studied by introducing disturbances into the systems. Based onH∞control theory and consensus theory, a condition is derived to guarantee the systems both reach consensus and have a certainH∞property. Finally, an example is worked out to demonstrate the effectiveness of the theoretical results.

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6

Kregel, Jan. "The discrete charm of the Washington consensus." Journal of Post Keynesian Economics 30, no. 4 (July 1, 2008): 541–60. http://dx.doi.org/10.2753/pke0160-3477300403.

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7

Trpevski, I., A. Stanoev, A. Koseska, and L. Kocarev. "Discrete-time distributed consensus on multiplex networks." New Journal of Physics 16, no. 11 (November 26, 2014): 113063. http://dx.doi.org/10.1088/1367-2630/16/11/113063.

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8

Almeida, João, Carlos Silvestre, and António M. Pascoal. "Continuous-time consensus with discrete-time communications." Systems & Control Letters 61, no. 7 (July 2012): 788–96. http://dx.doi.org/10.1016/j.sysconle.2012.04.004.

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9

Shih, Chih-Wen, and Jui-Pin Tseng. "Global consensus for discrete-time competitive systems." Chaos, Solitons & Fractals 41, no. 1 (July 2009): 302–10. http://dx.doi.org/10.1016/j.chaos.2007.12.005.

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10

Jia, Xiao, Laihong Hu, Fujun Feng, and Jun Xu. "Robust H∞ Consensus Control for Linear Discrete-Time Swarm Systems with Parameter Uncertainties and Time-Varying Delays." International Journal of Aerospace Engineering 2019 (July 24, 2019): 1–16. http://dx.doi.org/10.1155/2019/7278531.

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Robust H∞ consensus control problems of linear swarm systems with parameter uncertainties and time-varying delays are investigated. In this literature, a linear consensus protocol for high-order discrete-time swarm systems is proposed. Firstly, the robust H∞ consensus control problem of discrete-time swarm systems is transformed into a robust H∞ control problem of a set of independent uncertain systems. Secondly, sufficient linear matrix inequality conditions for robust H∞ consensus analysis of discrete-time swarm systems are given by the stability theory, and a H∞ performance level γ is determined meanwhile. Thirdly, the convergence result is derived as a final consensus value of swarm systems. Finally, numerical examples are presented to demonstrate theoretical results.

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11

Zhu, Wei. "Consensus of Discrete Time Second-Order Multiagent Systems with Time Delay." Discrete Dynamics in Nature and Society 2012 (2012): 1–9. http://dx.doi.org/10.1155/2012/390691.

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The consensus problem for discrete time second-order multiagent systems with time delay is studied. Some effective methods are presented to deal with consensus problems in discrete time multiagent systems. A necessary and sufficient condition is established to ensure consensus. The convergence rate for reaching consensus is also estimated. It is shown that arbitrary bounded time delay can safely be tolerated. An example is presented to illustrate the theoretical result.

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12

Zhang, Guoliang, Jun Xu, Jing Zeng, Jianxiang Xi, and Wenjun Tang. "Consensus of high-order discrete-time linear networked multi-agent systems with switching topology and time delays." Transactions of the Institute of Measurement and Control 39, no. 8 (February 15, 2016): 1182–94. http://dx.doi.org/10.1177/0142331216629197.

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In this paper, the consensus problem for high-order discrete-time networked multi-agent systems (D-NMAS) is investigated by distributed feedback protocols. By constructing the self-feedback matrix and the neighbouring feedback matrix for networks, consensus protocols are designed under three different cases and the corresponding convergence analysis is provided. Consensus convergence results of networks are provided by three final consensus values, which are related to self-feedback matrices, initial states of networks and the topology of networks, not related to time delays. In the first case where a directed network with a fixed topology is concerned, the high-order discrete-time consensus problem is studied as an example, and a sufficient and necessary condition is obtained. In the scenario with directed networks with switching topology, a sufficient condition guaranteeing the consensus of high-order D-NMAS is derived, after the consensus analysis is transformed into stability analysis. As for directed networks with switching topology and time delays, the discrete-time stability model with time delays is converted into a general discrete-time stability model by an augmented method and the sufficient condition is provided to achieve consensus for directed networks. Furthermore, the sufficient conditions determining the neighbouring feedback matrix are independent of the number of agents. Two numerical examples are provided to demonstrate the correctness and effectiveness of the theoretical results.

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13

Xiao, Li, Xiaofeng Liao, and Huiwei Wang. "Cluster Consensus on Discrete-Time Multi-Agent Networks." Abstract and Applied Analysis 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/274735.

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Nowadays, multi-agent networks are ubiquitous in the real world. Over the last decade, consensus has received an increasing attention from various disciplines. This paper investigates cluster consensus for discrete-time multi-agent networks. By utilizing a special coupling matrix and the Kronecker product, a criterion based on linear matrix inequality (LMI) is obtained. It is shown that the addressed discrete-time multi-agent networks achieve cluster consensus if a certain LMI is feasible. Finally, an example is given to demonstrate the effectiveness of the proposed criterion.

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14

Gao, Yanping, Bo Liu, Min Zuo, Tongqiang Jiang, and Junyan Yu. "Consensus of Continuous-Time Multiagent Systems with General Linear Dynamics and Nonuniform Sampling." Mathematical Problems in Engineering 2013 (2013): 1–7. http://dx.doi.org/10.1155/2013/718759.

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This paper studies the consensus problem of multiple agents with general linear continuous-time dynamics. It is assumed that the information transmission among agents is intermittent; namely, each agent can only obtain the information of other agents at some discrete times, where the discrete time intervals may not be equal. Some sufficient conditions for consensus in the cases of state feedback and static output feedback are established, and it is shown that if the controller gain and the upper bound of discrete time intervals satisfy certain linear matrix inequality, then consensus can be reached. Simulations are performed to validate the theoretical results.

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15

MIYAKE, Shiori, Naoki HAYASHI, and Shigemasa TAKAI. "Discrete-Time Average Consensus with Multi-Hop Communication." SICE Journal of Control, Measurement, and System Integration 9, no. 5 (2016): 187–91. http://dx.doi.org/10.9746/jcmsi.9.187.

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16

HUANG, Qin-Zhen. "Consensus Analysis of Multi-agent Discrete-time Systems." Acta Automatica Sinica 38, no. 7 (2012): 1127. http://dx.doi.org/10.3724/sp.j.1004.2012.01127.

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17

Cavalcante, R. L. G., and B. Mulgrew. "Adaptive Filter Algorithms for Accelerated Discrete-Time Consensus." IEEE Transactions on Signal Processing 58, no. 3 (March 2010): 1049–58. http://dx.doi.org/10.1109/tsp.2009.2032450.

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18

Hu, Wenjun, Gang Zhang, Zhongjun Ma, and Binbin Wu. "Partial Component Consensus of Discrete-Time Multiagent Systems." Mathematical Problems in Engineering 2019 (April 16, 2019): 1–5. http://dx.doi.org/10.1155/2019/4725418.

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The multiagent system has the advantages of simple structure, strong function, and cost saving, which has received wide attention from different fields. Consensus is the most basic problem in multiagent systems. In this paper, firstly, the problem of partial component consensus in the first-order linear discrete-time multiagent systems with the directed network topology is discussed. Via designing an appropriate pinning control protocol, the corresponding error system is analyzed by using the matrix theory and the partial stability theory. Secondly, a sufficient condition is given to realize partial component consensus in multiagent systems. Finally, the numerical simulations are given to illustrate the theoretical results.

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19

HUANG, Qin-Zhen. "Consensus Analysis of Multi-agent Discrete-time Systems." Acta Automatica Sinica 38, no. 7 (July 2012): 1127–33. http://dx.doi.org/10.1016/s1874-1029(11)60287-5.

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20

Ji, Chengda, Yue Shen, Marin Kobilarov, and Dennice F. Gayme. "Augmented Consensus Algorithm for Discrete-time Dynamical Systems." IFAC-PapersOnLine 52, no. 20 (2019): 115–20. http://dx.doi.org/10.1016/j.ifacol.2019.12.140.

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21

FORTUNATO, SANTO. "THE KRAUSE–HEGSELMANN CONSENSUS MODEL WITH DISCRETE OPINIONS." International Journal of Modern Physics C 15, no. 07 (September 2004): 1021–29. http://dx.doi.org/10.1142/s0129183104006479.

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The consensus model of Krause and Hegselmann can be naturally extended to the case in which opinions are integer instead of real numbers. Our algorithm is much faster than the original version and thus more suitable for applications. For the case of a society in which everybody can talk to everybody else, we find that the chance to reach consensus is much higher as compared to other models; if the number of possible opinions Q≤7, in fact, consensus is always reached, which might explain the stability of political coalitions with more than three or four parties. For Q>7 the number S of surviving opinions is approximately the same, independent of the size N of the population, as long as Q<N. We considered as well the more realistic case of a society structured like a Barabási–Albert network; here the consensus threshold depends on the out-degree of the nodes and we find a simple scaling law for S, as observed for the discretized Deffuant model.

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22

Song, Yun-Zhong. "Consensus of agents with mixed linear discrete dynamics." International Journal of Control, Automation and Systems 14, no. 4 (August 2016): 1139–43. http://dx.doi.org/10.1007/s12555-015-0062-7.

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23

Saburov, Mansoor. "Reaching a Consensus in Multi-Agent Systems: A Time Invariant Nonlinear Rule." Journal of Education and Vocational Research 4, no. 5 (May 30, 2013): 130–33. http://dx.doi.org/10.22610/jevr.v4i5.110.

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Multi-Agent Systems (MAS) have attracted more and more interest in recent years. Most researches in the study of discrete-time MAS, presented in the past few years, have considered linear cooperative rules. However, local interactions between agents are more likely to be governed by nonlinear rules. In this paper, we investigate the consensus of discrete-time MAS with time invariant nonlinear cooperative rules. Based on our presented nonlinear model, we show a consensus in the discrete-time MAS. Our model generalizes a classical time invariant De Groot model. It seems that, unlike a linear case, a consensus can be easily achieved a nonlinear case.

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24

Xu, Bingbing, Lixin Gao, Yan Zhang, and Xiaole Xu. "Leader-Following Consensus Stability of Discrete-Time Linear Multiagent Systems with Observer-Based Protocols." Abstract and Applied Analysis 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/357971.

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We consider the leader-following consensus problem of discrete-time multiagent systems on a directed communication topology. Two types of distributed observer-based consensus protocols are considered to solve such a problem. The observers involved in the proposed protocols include full-order observer and reduced-order observer, which are used to reconstruct the state variables. Two algorithms are provided to construct the consensus protocols, which are based on the modified discrete-time algebraic Riccati equation and Sylvester equation. In light of graph and matrix theory, some consensus conditions are established. Finally, a numerical example is provided to illustrate the obtained result.

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25

Yuan, Yi, Shamrie Sainin Mohd, and Yanhui Zhu. "Bipartite Consensus of Linear Discrete-Time Multiagent Systems with Exogenous Disturbances under Competitive Networks." Complexity 2021 (May 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/9983999.

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This paper investigates the bipartite consensus of linear discrete-time multiagent systems (MASs) with exogenous disturbances. A discrete-time disturbance-observer- (DTDO-) based technology is involved for attenuating the exogenous disturbances. And both the state feedback and observer-based output feedback bipartite consensus protocols are proposed by using the DTDO method. It turned out that bipartite consensus can be realized under the given protocols if the topology is connected and structurally balanced. Finally, numerical simulations are presented to illustrate the theoretical findings.

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26

Ge, Yanrong, Yangzhou Chen, Yaxiao Zhang, and Zhonghe He. "State Consensus Analysis and Design for High-Order Discrete-Time Linear Multiagent Systems." Mathematical Problems in Engineering 2013 (2013): 1–13. http://dx.doi.org/10.1155/2013/192351.

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The paper deals with the state consensus problem of high-order discrete-time linear multiagent systems (DLMASs) with fixed information topologies. We consider three aspects of the consensus analysis and design problem: (1) the convergence criteria of global state consensus, (2) the calculation of the state consensus function, and (3) the determination of the weighted matrix and the feedback gain matrix in the consensus protocol. We solve the consensus problem by proposing a linear transformation to translate it into a partial stability problem. Based on the approach, we obtain necessary and sufficient criteria in terms of Schur stability of matrices and present an analytical expression of the state consensus function. We also propose a design process to determine the feedback gain matrix in the consensus protocol. Finally, we extend the state consensus to the formation control. The results are explained by several numerical examples.

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27

Zhao, Huanyu, Hongbiao Zhou, and Zhongyi Tang. "Output Feedback Control for Couple-Group Consensus of Multiagent Systems." Abstract and Applied Analysis 2014 (2014): 1–7. http://dx.doi.org/10.1155/2014/239872.

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This paper deals with the couple-group consensus problem for multiagent systems via output feedback control. Both continuous- and discrete-time cases are considered. The consensus problems are converted into the stability problem of the error systems by the system transformation. We obtain two necessary and sufficient conditions of couple-group consensus in different forms for each case. Two different algorithms are used to design the control gains for continuous- and discrete-time case, respectively. Finally, simulation examples are given to show the effectiveness of the proposed results.

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28

Jiang, He, and Dongsheng Yang. "Output Consensus Regulation for State-Unmeasurable Discrete-Time Multiagent Systems with External Disturbances." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/158702.

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This paper deals with the output consensus regulation problem for discrete-time multiagent systems with state-unmeasurable agents and external disturbances under directed communication network topologies. Firstly, the mathematical model for the output consensus problem of discrete-time multiagent systems is deduced and formulated via making matrix transformation. Then, based on state observers, a novel output consensus protocol with dynamic compensator which is used as observer for the exosystem is proposed to solve this problem. Some knowledge of matrix theory and graph theory is introduced to design protocol parameters and the convergence of output consensus errors is proved. Finally, a numerical simulation example is shown to verify the effectiveness of the proposed protocol design.

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29

Hristova, Snezhana, Kremena Stefanova, and Angel Golev. "Dynamic modeling of discrete leader-following consensus with impulses." AIMS Mathematics 4, no. 5 (2019): 1386–402. http://dx.doi.org/10.3934/math.2019.5.1386.

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30

Franceschelli, Mauro, and Andrea Gasparri. "Multi-stage discrete time and randomized dynamic average consensus." Automatica 99 (January 2019): 69–81. http://dx.doi.org/10.1016/j.automatica.2018.10.009.

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31

Mahmoud, Magdi S., and Gulam Dastagir Khan. "LMI consensus condition for discrete-time multi-agent systems." IEEE/CAA Journal of Automatica Sinica 5, no. 2 (March 2018): 509–13. http://dx.doi.org/10.1109/jas.2016.7510016.

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32

Saburov, M., and K. Saburov. "Reaching a consensus: a discrete nonlinear time-varying case." International Journal of Systems Science 47, no. 10 (January 8, 2015): 2449–57. http://dx.doi.org/10.1080/00207721.2014.998743.

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33

Zhao, Huanyu, and Shumin Fei. "Distributed consensus for discrete-time heterogeneous multi-agent systems." International Journal of Control 91, no. 6 (April 13, 2017): 1376–84. http://dx.doi.org/10.1080/00207179.2017.1315650.

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34

Wang, Zhenhua, Huanshui Zhang, Xinmin Song, and Huaxiang Zhang. "Consensus problems for discrete-time agents with communication delay." International Journal of Control, Automation and Systems 15, no. 4 (June 27, 2017): 1515–23. http://dx.doi.org/10.1007/s12555-015-0446-8.

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35

Zhao, Meng, Ting Liu, Jia Su, and Meng-Ying Liu. "A Method Adjusting Consistency and Consensus for Group Decision-Making Problems with Hesitant Fuzzy Linguistic Preference Relations Based on Discrete Fuzzy Numbers." Complexity 2018 (July 12, 2018): 1–17. http://dx.doi.org/10.1155/2018/9345609.

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In each hesitant fuzzy linguistic preference relation, experts may express their opinions through comparison linguistic information combined with a discrete fuzzy number. In this paper, a hesitant fuzzy linguistic computational model based on discrete fuzzy numbers whose support is a subset of consecutive natural numbers is proposed, which enriches the flexibility of group decision-making. First, some main concepts related to discrete fuzzy numbers and an aggregation function of individual subjective linguistic preference relations are introduced. Then, a consistency measure is presented to check and improve the consistency in a given matrix. Further, in order to achieve the predefined degree of consensus and to arrive at the final result, a consensus-reaching process based on the interactive feedback mechanism is defined. Meanwhile, a revised formula is introduced to calculate the consistency and the degree of consensus in a preference relation matrix. Besides, an illustrative example and comparative analysis are conducted through the proposed calculation process and the optimization algorithm. Finally, the analysis on the threshold values is made to help the decision-maker determine critical consensus level. The proposed method can address both consistency and consensus, and the results confirmed the effectiveness of the proposed method and its potential use in the qualitative decision-making problems.

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36

Liang, Shuang, Zhongxin Liu, and Zengqiang Chen. "Leader-following H∞ consensus of discrete-time nonlinear multi-agent systems based upon output feedback control." Transactions of the Institute of Measurement and Control 42, no. 7 (December 23, 2019): 1323–33. http://dx.doi.org/10.1177/0142331219889555.

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In this paper, the leader-following [Formula: see text] consensus problem for discrete-time nonlinear multi-agent systems with delay and parameter uncertainty is investigated, with the objective of designing an output feedback protocol such that the multi-agent system achieves leader-following consensus and has a prescribed [Formula: see text] performance level. By model transforming, the leader-following consensus control problem is converted into robust [Formula: see text] control problem. Based on the Lyapunov function technology and the linear matrix inequality method, some new sufficient conditions are derived to guarantee the consensus of discrete-time nonlinear multi-agent systems. The feedback gain matrix and the optimal [Formula: see text] performance index are obtained in terms of linear matrix inequalities. Finally, numerical examples are provided to illustrate the effectiveness of the theoretical results.

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37

Ding, Lei. "Consensus of Discrete-Time Second-Order Multi-Agent Systems with Partial Information Transmission." Applied Mechanics and Materials 457-458 (October 2013): 1069–73. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.1069.

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This paper investigates the consensus problem of multi-agent systems with partial information transmission under an undirected topology. A distributed consensus protocol is proposed with local velocity feedback and the position information from neighbors. The consensus problem is converted to the stabilization problem by transforming the original systems into a reduced order state system. Then, by using graph theory and Jurys stability test, a sufficient and necessary condition for consensus of multi-agent systems is derived. An example is given to illustrate the effectiveness of the presented results.

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38

Yang, Haoyue, Hao Zhao, Zhuping Wang, and Xuemei Zhou. "ℋ∞ leader-following consensus of multi-agent systems with channel fading under switching topologies: a semi-Markov kernel approach." Intelligence & Robotics 2, no. 3 (2022): 223–43. http://dx.doi.org/10.20517/ir.2022.19.

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This paper focuses on the leader-following consensus problem of discrete-time multi-agent systems subject to channel fading under switching topologies. First, a topology switching-based channel fading model is established to describe the information fading of the communication channel among agents, which also considers the channel fading from leader to follower and from follower to follower. It is more general than models in the existing literature that only consider follower-to-follower fading. For discrete multi-agent systems, the existing literature usually adopts time series or Markov process to characterize topology switching while ignoring the more general semi-Markov process. Based on the advantages and properties of semi-Markov processes, discrete semi-Markov jump processes are adopted to model network topology switching. Then, the semi-Markov kernel approach for handling discrete semi-Markov jumping systems is exploited and some novel sufficient conditions to ensure the leader-following mean square consensus of closed-loop systems are derived. Furthermore, the distributed consensus protocol is proposed by means of the stochastic Lyapunov stability theory so that the underlying systems can achieve ℋ∞ consensus performance index. In addition, the proposed method is extended to the scenario where the semi-Markov kernel of semi-Markov switching topologies is not completely accessible. Finally, a simulation example is given to verify the results proposed in this paper. Compared with the existing literature, the method in this paper is more effective and general.

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39

Zhang, Yinyan, and Shuai Li. "Perturbing consensus for complexity: A finite-time discrete biased min-consensus under time-delay and asynchronism." Automatica 85 (November 2017): 441–47. http://dx.doi.org/10.1016/j.automatica.2017.08.014.

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40

Kim, Won Il, Rong Xiong, Qiuguo Zhu, and Jun Wu. "Multiagent Consensus Control under Network-Induced Constraints." Journal of Applied Mathematics 2013 (2013): 1–4. http://dx.doi.org/10.1155/2013/601652.

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Mean consensus problem is studied using a class of discrete time multiagent systems in which information exchange is subjected to some network-induced constraints. These constraints include package dropout, time delay, and package disorder. Using Markov jump system method, the necessary and sufficient condition of mean square consensus is obtained and a design procedure is presented such that multiagent systems reach mean square consensus.

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41

Yan, Yamin, Sonja Stüdli, Maria M. Seron, and Richard H. Middleton. "Disruption via Grounding and Countermeasures in Discrete-Time Consensus Networks." IFAC-PapersOnLine 53, no. 2 (2020): 2944–50. http://dx.doi.org/10.1016/j.ifacol.2020.12.970.

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42

Masroor, Suhaib, Chen Peng, Zain Anwar Ali, and Jin Zhang. "Discrete Time Consensus of Leader Following BLDC Motor with Delay." International Journal of Modeling and Optimization 6, no. 3 (June 2016): 150–55. http://dx.doi.org/10.7763/ijmo.2016.v6.520.

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43

Li, Zonggang, Yingmin Jia, Junping Du, and Fashan Yu. "Algebraic Criteria for Consensus Problems of Discrete-Time Networked Systems." IFAC Proceedings Volumes 41, no. 2 (2008): 1502–9. http://dx.doi.org/10.3182/20080706-5-kr-1001.00257.

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44

Franceschelli, Mauro, Alessandro Giua, and Carla Seatzu. "A Gossip-Based Algorithm for Discrete Consensus Over Heterogeneous Networks." IEEE Transactions on Automatic Control 55, no. 5 (May 2010): 1244–49. http://dx.doi.org/10.1109/tac.2010.2042360.

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45

Chen, Yao, Jinhu Lü, Fengling Han, and Xinghuo Yu. "On the cluster consensus of discrete-time multi-agent systems." Systems & Control Letters 60, no. 7 (July 2011): 517–23. http://dx.doi.org/10.1016/j.sysconle.2011.04.009.

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46

Zhao, Huanyu, Ju H. Park, Yulin Zhang, and Hao Shen. "Distributed output feedback consensus of discrete-time multi-agent systems." Neurocomputing 138 (August 2014): 86–91. http://dx.doi.org/10.1016/j.neucom.2014.02.043.

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47

Han, Fei, Guoliang Wei, Yan Song, and Wangyan Li. "Distributed H∞-consensus filtering for piecewise discrete-time linear systems." Journal of the Franklin Institute 352, no. 5 (May 2015): 2029–46. http://dx.doi.org/10.1016/j.jfranklin.2015.02.010.

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48

Chen, Yao, Jinhu Lü, and Zongli Lin. "Consensus of discrete-time multi-agent systems with transmission nonlinearity." Automatica 49, no. 6 (June 2013): 1768–75. http://dx.doi.org/10.1016/j.automatica.2013.02.021.

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49

Wang, Jing, Xiao-hong Nian, and Hai-bo Wang. "Consensus and formation control of discrete-time multi-agent systems." Journal of Central South University of Technology 18, no. 4 (July 10, 2011): 1161–68. http://dx.doi.org/10.1007/s11771-011-0818-z.

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50

Li, Lulu, Daniel W. C. Ho, and Yurong Liu. "Discrete-time multi-agent consensus with quantization and communication delays." International Journal of General Systems 43, no. 3-4 (March 4, 2014): 319–31. http://dx.doi.org/10.1080/03081079.2014.892253.

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