Literatura académica sobre el tema "Discrete Consensus"
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Artículos de revistas sobre el tema "Discrete Consensus"
Malinowska, Agnieszka B., Ewa Schmeidel y Małgorzata Zdanowicz. "Discrete leader-following consensus". Mathematical Methods in the Applied Sciences 40, n.º 18 (11 de agosto de 2017): 7307–15. http://dx.doi.org/10.1002/mma.4530.
Texto completoChen, Yao y Jinhu Lü. "Delay-induced discrete-time consensus". Automatica 85 (noviembre de 2017): 356–61. http://dx.doi.org/10.1016/j.automatica.2017.07.059.
Texto completoZhu, Minghui y Sonia Martínez. "Discrete-time dynamic average consensus". Automatica 46, n.º 2 (febrero de 2010): 322–29. http://dx.doi.org/10.1016/j.automatica.2009.10.021.
Texto completoMontijano, Eduardo, Juan Ignacio Montijano, Carlos Sagüés y Sonia Martínez. "Robust discrete time dynamic average consensus". Automatica 50, n.º 12 (diciembre de 2014): 3131–38. http://dx.doi.org/10.1016/j.automatica.2014.10.005.
Texto completoWang, Xiaoping y 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.
Texto completoKregel, Jan. "The discrete charm of the Washington consensus". Journal of Post Keynesian Economics 30, n.º 4 (1 de julio de 2008): 541–60. http://dx.doi.org/10.2753/pke0160-3477300403.
Texto completoTrpevski, I., A. Stanoev, A. Koseska y L. Kocarev. "Discrete-time distributed consensus on multiplex networks". New Journal of Physics 16, n.º 11 (26 de noviembre de 2014): 113063. http://dx.doi.org/10.1088/1367-2630/16/11/113063.
Texto completoAlmeida, João, Carlos Silvestre y António M. Pascoal. "Continuous-time consensus with discrete-time communications". Systems & Control Letters 61, n.º 7 (julio de 2012): 788–96. http://dx.doi.org/10.1016/j.sysconle.2012.04.004.
Texto completoShih, Chih-Wen y Jui-Pin Tseng. "Global consensus for discrete-time competitive systems". Chaos, Solitons & Fractals 41, n.º 1 (julio de 2009): 302–10. http://dx.doi.org/10.1016/j.chaos.2007.12.005.
Texto completoJia, Xiao, Laihong Hu, Fujun Feng y 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 (24 de julio de 2019): 1–16. http://dx.doi.org/10.1155/2019/7278531.
Texto completoTesis sobre el tema "Discrete Consensus"
FRANCESCHELLI, MAURO. "Consensus Algorithms for Estimation and Discrete Averaging in Networked Control Systems". Doctoral thesis, Università degli Studi di Cagliari, 2011. http://hdl.handle.net/11584/266297.
Texto completoPedroncelli, Giovanni. "Distributed Discrete Consensus Algorithms: Theory and Applications for the Task Assignment Problem". Doctoral thesis, Università degli studi di Trieste, 2015. http://hdl.handle.net/10077/10975.
Texto completoDistributed computation paradigms belong to a research field of increasing interest. Using these algorithms will allow to exploit the capabilities of large scale networks and systems in the near future. Relevant information for the resolution of a problem are distributed among a network of agents with limited memory and computation capability; the problem is solved only by means of local computation and message exchange between neighbour agents. In this thesis we consider the multi-agent assignment problem dealt with distributed computation: a network of agents has to cooperatively negotiate the assignment of a number of tasks by applying a distributed discrete consensus algorithm which defines how the agents exchange information. Consensus algorithms are dealt with always more frequently in the related scientific literature. Therefore, in the first chapter of this thesis we present a related literature review containing some of the most interesting works concerning distributed computation and, in particular, distributed consensus algorithms: some of these works deal with the theory of consensus algorithms, in particular convergence properties, others deal with applications of these algorithms. In the second chapter the main contribution of this thesis is presented: aniterative distributed discrete consensus algorithm based on the resolution of local linear integer optimization problems (L-ILPs) to be used for the multi-agent assignment problem. The algorithm is characterized by theorems proving convergence to a final solution and the value of the convergence time expressed in terms of number of iterations. The chapter is concluded by a performance analysis by means of the results of simulations performed with Matlab software. All the results are presented considering two different network topologies in order to model two different real life scenarios for the connection among agents. The third chapter presents an interesting application of the proposed algorithm: a network of charging stations (considered as agents) has to reach a consensus on the assignment of a number of Electric Vehicles (EVs) requiring to be recharged. In this application the algorithm proposed in the previous chapter undergoes several modifications in order to model effectively this case: considering the inter-arrival times of vehicles to a charging station, a non-linear element appears in the objective function and therefore a novel algorithm to be performed before the assignment algorithm is presented; this algorithm defines the order in which the assigned vehicles have to reach a charging station. Moreover, a communication protocol is proposed by which charging stations and vehicles can communicate and exchange information also allowing charging stations to send to each assigned vehicle the maximum waiting time which can pass before a vehicle loses its right to be recharged. The chapter ends with an example of application of the rivisited assignment algorithm. In the fourth and last chapter, we present an application in an industrial environment: a network of Autonomous Guided Vehicles (AGVs) in a warehouse modeled as a graph has to perform the distributed discrete consensus algorithm in order to assign themselves a set of destinations in which some tasks are located. This application deals not only with the task assignment problem but also with the following destination reaching problem: therefore a distributed coordination algorithm is proposed which allows the AGVs to move into the warehouse avoiding collisions and deadlock. An example of the control strategy application involving both the assignment and coordination algorithms concludes this chapter.
XXVII Ciclo
1986
Saberian, Aminmohammad. "Applying adjacency based control to distribution networks". Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/126755/1/Aminmohammad_Saberian_Thesis.pdf.
Texto completoRicciardi, Celsi Lorenzo. "Commande non linéaire multi-agents : applications aux systèmes en réseau". Thesis, Université Paris-Saclay (ComUE), 2018. http://www.theses.fr/2018SACLS017/document.
Texto completoThe objective of this PhD thesis is (i) to investigate and develop methods for the analysis and design of linear and nonlinear networked control systems and (ii) to show the potential of such approaches in relevant complex applications. In this respect, multi-agent systems theory, algebraic graph theory and consensus are the most interesting methodological tools, and specific attention is paid to the characterization of the relationships between, on the one hand, the topology of the communication graph that underlies the evolution of the considered multiagent system and, on the other hand, the spectral properties of the Laplacian matrix associated with the graph itself. The control of a group of autonomous agents is investigated from different perspectives. The main control objective is to make sure that the agents work together in a cooperative fashion, where cooperation accounts for the close relationship among all agents in the team, with information sharing playing an important role. In particular, various problems regarding consensus/agreement/synchronization/rendezvous are investigated with the specific aim of driving a group of agents to some common state. Consensus is investigated in a discrete-time setting due to the fact that the system dynamics is normally continuous while the measurements and control inputs might only be made in a sampled-data setting. Moreover, game theory is relied upon in order to cope with distributed multi-agent coordination problems, with application to Software Defined Networks. In this respect, it can be shown that, under properly designed protocols, the players converge to a unique Wardrop equilibrium. We focus on distributed control, since this approach shows obvious benefits over centralization, such as scalability and robustness. Yet, it also has its own drawbacks: among all, one drawback is that each agent cannot effectively predict the overall group behaviour based on only local information. Some attention is also devoted to the need for securing power grids against the danger of cyber-physical attacks through the development of distributed intelligence technologies accompanied by appropriate security enforcements. In this respect, based on realistic power network topologies, we briefly present the design of a protection scheme against closed-loop single-point and multi-point dynamic load altering attacks. This is done by formulating and solving a non-convex optimization problem subject to a Lyapunov stability constraint for the autonomous multiagent representation of a power system obtained after linearization and application of the attack and frequency control laws. Eventually, we show some other results achieved in terms of the exact steeering of finite sampled nonlinear dynamics with input delays, of sampled-data stabilization and quasi-halo orbit following around the L₂ translunar libration point, and of heuristic algorithms based on multi-agent reinforcement learning methods capable of performing optimal adaptive Quality of Service/Quality of Experience control in model-free scenarios
Ajwad, Syed Ali. "Distributed control of multi-agent systems under communication constraints : application to robotics". Thesis, Poitiers, 2020. http://www.theses.fr/2020POIT2264.
Texto completoMulti-agent systems (MAS) have gained much popularity due to their vast range of applications. MAS is deployed to achieve more complex goals which could not be realized by a single agent alone. Communication and information exchange among the agents in a MAS is crucial to control its cooperative behavior. Agents share their information with their neighbors to reach a common objective, thus do not require any central monitoring unit. However, the communication among the agents is subject to various practical constraints. These constraints include irregular and asynchronous sampling periods and the availability of partial states only. Such constraints pose significant theoretical and practical challenges. In this thesis, we investigate two fundamental problems related to distributed cooperative control, namely consensus and formation control, of double-integrator MAS under these constraints. It is considered that each agent in the network can measure and transmit its position state only at nonuniform and asynchronous sampling instants. Moreover, the velocity and acceleration are not available. First, we study the problem of distributed control of leader-following consensus. A continuous-discrete time observer based leader-following algorithm is proposed. The observer estimates the position and velocity of the agent and its neighbor in continuous time from the available sampled position data. Then these estimated states are used for the computation of the control input. Both fixed and switching topology scenarios are discussed. Secondly, a consensus based distributed formation tracking protocol is designed to achieve both fixed and time-varying formation patterns. Collision avoidance problem is also studied in this thesis. An Artificial Potential Function (APF) based collision avoidance mechanism is incorporated with the formation tracking algorithm to prevent collisions between the agents while converging to a desired position. Finally, the proposed algorithms are applied on a multi-robot network, consisting of differential drive robots using Robot Operating System (ROS). A new scheme is proposed to deal with nonholonomic constraints of the robot. Efficiency of the designed algorithms and their effectiveness in real world applications are shown through both simulation and hardware results
Tseng, Jui-Pin y 曾睿彬. "Global Consensus for Discrete-time Competitive System". Thesis, 2005. http://ndltd.ncl.edu.tw/handle/29666934989014479945.
Texto completo國立交通大學
應用數學系所
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A discrete-time competitive system is studied. We are interested in how the dynamics of the system reach global consensus. Analytical arguments are developed to conclude that every orbit converges to a point as time tends to infinity, without knowing a Lyapunov function.
RICCIARDI, CELSI LORENZO. "Commande non linéaire multi-agents : applications aux systèmes en réseau (nonlinear multi-agent control with application to networked systems)". Doctoral thesis, 2018. http://hdl.handle.net/11573/1065175.
Texto completoLibros sobre el tema "Discrete Consensus"
Hauswald, Rico y Lara Keuck. Indeterminacy in medical classification: On continuity, uncertainty, and vagueness. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198722373.003.0005.
Texto completoAnderson, Greg. Our Athenian Yesterdays. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780190886646.003.0002.
Texto completoCapítulos de libros sobre el tema "Discrete Consensus"
Mahmoud, MagdiSadek y Bilal J. Karaki. "Scaled-Type Consensus". En Control Design of Multiagent Discrete-Time Systems, 83–125. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90940-6_3.
Texto completoMahmoud, MagdiSadek y Bilal J. Karaki. "Leader-Following Consensus". En Control Design of Multiagent Discrete-Time Systems, 241–85. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-90940-6_7.
Texto completoZhang, Yinyan y Shuai Li. "High-Order Discrete-Time Consensus". En Machine Behavior Design And Analysis, 21–44. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3231-3_3.
Texto completoZhang, Yinyan y Shuai Li. "Discrete-Time Biased Min-Consensus". En Machine Behavior Design And Analysis, 73–96. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3231-3_5.
Texto completoValentini, Gabriele. "Discrete Consensus Achievement in Artificial Systems". En Achieving Consensus in Robot Swarms, 9–32. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-53609-5_2.
Texto completoZrour, Rita, Gaëlle Largeteau-Skapin y Eric Andres. "Optimal Consensus Set for Annulus Fitting". En Discrete Geometry for Computer Imagery, 358–68. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19867-0_30.
Texto completoDou, Quansheng, Zhongzhi Shi y Yuehao Pan. "Noisy Control About Discrete Liner Consensus Protocol". En Intelligent Information Processing VIII, 235–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48390-0_24.
Texto completoDeplano, Diego, Mauro Franceschelli y Alessandro Giua. "Discrete-Time Dynamic Consensus on the Max Value". En Lecture Notes in Control and Information Sciences - Proceedings, 367–83. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85318-1_22.
Texto completoZhou, Panpan y Ben M. Chen. "Leader-Following Output Consensus of Discrete-Time Heterogeneous Systems". En Complex Systems: Spanning Control and Computational Cybernetics: Foundations, 73–87. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-99776-2_6.
Texto completoPatel, Keyurkumar y Axaykumar Mehta. "Event-Triggered Discrete-Time Higher Order Sliding Mode Protocol for Leader-Following Consensus of Homogeneous DMAS". En Discrete-Time Sliding Mode Protocols for Discrete Multi-Agent System, 97–109. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-6311-9_6.
Texto completoActas de conferencias sobre el tema "Discrete Consensus"
Casbeer, David W., Randy Beard y A. Lee Swindlehurst. "Discrete double integrator consensus". En 2008 47th IEEE Conference on Decision and Control. IEEE, 2008. http://dx.doi.org/10.1109/cdc.2008.4739168.
Texto completoZhou, Jing y Qian Wang. "Distributed discrete-time nonlinear consensus protocols". En 2009 Joint 48th IEEE Conference on Decision and Control (CDC) and 28th Chinese Control Conference (CCC). IEEE, 2009. http://dx.doi.org/10.1109/cdc.2009.5400537.
Texto completoDeshmukh, Raj, Cheolhyeon Kwon y Inseok Hwang. "Optimal discrete-time Kalman Consensus Filter". En 2017 American Control Conference (ACC). IEEE, 2017. http://dx.doi.org/10.23919/acc.2017.7963859.
Texto completoMengjie Zhou, Jianping He, Peng Cheng y Jiming Chen. "Discrete average consensus with bounded noise". En 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760718.
Texto completoBecchetti, L., A. Clementi, E. Natale, F. Pasquale y L. Trevisan. "Stabilizing Consensus with Many Opinions". En Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974331.ch46.
Texto completoOstaszewska, Urszula, Ewa Schmeidel y Małgorzata Zdanowicz. "Leader-following consensus on discrete time scales". En INTERNATIONAL CONFERENCE OF NUMERICAL ANALYSIS AND APPLIED MATHEMATICS (ICNAAM 2017). Author(s), 2018. http://dx.doi.org/10.1063/1.5044162.
Texto completoFanti, Maria Pia, Agostino M. Mangini, Giovanni Pedroncelli y Walter Ukovich. "Discrete consensus for asynchronous distributed task assignment". En 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 2016. http://dx.doi.org/10.1109/cdc.2016.7798278.
Texto completoFranceschelli, Mauro y Andrea Gasparri. "Multi-stage discrete time dynamic average consensus". En 2016 IEEE 55th Conference on Decision and Control (CDC). IEEE, 2016. http://dx.doi.org/10.1109/cdc.2016.7798381.
Texto completoXia, Weiguo, Guodong Shi, Ziyang Meng, Ming Cao y Karl Henrik Johansson. "Balance conditions in discrete-time consensus algorithms". En 2017 IEEE 56th Annual Conference on Decision and Control (CDC). IEEE, 2017. http://dx.doi.org/10.1109/cdc.2017.8263753.
Texto completoFanti, Maria Pia, Mauro Franceschelli, Agostino Marcello Mangini, Giovanni Pedroncelli y Walter Ukovich. "Discrete consensus in networks with constrained capacity". En 2013 IEEE 52nd Annual Conference on Decision and Control (CDC). IEEE, 2013. http://dx.doi.org/10.1109/cdc.2013.6760177.
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