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Articles de revues sur le sujet « Distribued control »

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Auteur : Grafiati

Publié le 4 mai 2024

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1

Nedić, Angelia, et Ji Liu. « Distributed Optimization for Control ». Annual Review of Control, Robotics, and Autonomous Systems 1, n^o 1 (28 mai 2018) : 77–103. http://dx.doi.org/10.1146/annurev-control-060117-105131.

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Advances in wired and wireless technology have necessitated the development of theory, models, and tools to cope with the new challenges posed by large-scale control and optimization problems over networks. The classical optimization methodology works under the premise that all problem data are available to a central entity (a computing agent or node). However, this premise does not apply to large networked systems, where each agent (node) in the network typically has access only to its private local information and has only a local view of the network structure. This review surveys the development of such distributed computational models for time-varying networks. To emphasize the role of the network structure in these approaches, we focus on a simple direct primal (sub)gradient method, but we also provide an overview of other distributed methods for optimization in networks. Applications of the distributed optimization framework to the control of power systems, least squares solutions to linear equations, and model predictive control are also presented.

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2

ME, E. Sankaran. « Distributed Control Systems in Food Processing ». International Journal of Trend in Scientific Research and Development Volume-3, Issue-1 (31 décembre 2018) : 27–30. http://dx.doi.org/10.31142/ijtsrd18921.

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3

França, Bruno, Emanuel Emmerik, Juliano Caldeira et Maurício Aredes. « Sliding Droop Control For Distributed Generation In Microgrids ». Eletrônica de Potência 22, n^o 4 (1 décembre 2017) : 429–39. http://dx.doi.org/10.18618/rep.2017.4.2726.

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4

Sapaty, P. S. « Symbiosis of Distributed Simulation and Control under Spatial Grasp Technology ». Mathematical machines and systems 3 (2020) : 23–48. http://dx.doi.org/10.34121/1028-9763-2020-3-23-48.

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We are witnessing rapidly growing world dynamics caused by climate change, military, religious and ethnic conflicts, terrorism, refugee flows and weapons proliferation, political and industrial restructuring too. Dealing with frequently emerging crises may need rapid integration of scattered heterogeneous resources into capable operational forces pursuing goals which may not be known in advance. Proper understanding and managing of unpredictable and crisis situations may need their detailed simulation at runtime and even ahead of it. The current paper aims at deep integration, actually symbiosis, of advanced simulation with live system control and management, which can be effectively organized in nationwide and world scale. It will be presenting the latest version of Spatial Grasp Technology (SGT) which is not based on traditional communicating parts or agents, as usual, but rather using self-spreading, self-replicating, and self-modifying higher-level code covering and matching distributed systems at runtime while providing global integrity, goal-orientation, and finding effective solutions. These spatial solutions are often hundreds of times shorter and simpler than with other approaches due to special recursive scenario language hiding traditional system management routines inside its parallel and distributed interpretation. The paper provides basics for deep integration, actually symbiosis, of different worlds allowing us to unite advanced distributed simulation with spatial parallel and fully distributed control, while doing all this within the same high-level and very simple Spatial Grasp formalism and its basic Spatial Grasp Language (SGL). It will also mention various SGT applications including economy, ecology, space research & conquest and security, where effective symbiosis of distributed interactive simulation with live control and management may provide a real breakthrough. SGL can be quickly implemented even within standard university environments by a group of system programmers, similar to its previous versions in different countries under the author’s supervision. The technology can be installed in numerous copies worldwide and deeply integrated with any other systems, actually acquiring unlimited power throughout the world.

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5

HARAMAKI, Shinya, Akihiro HAYASHI, Toshifumi SATAKE et Shigeru AOMURA. « Distributed Cooperative Control System for Multi-jointed Redundant Manipulator(Control Theory and Application,Session : MA1-B) ». Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2004.4 (2004) : 21. http://dx.doi.org/10.1299/jsmeicam.2004.4.21_2.

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6

Moreno Navarro, I., E. Martín Candelario et M. Álvarez Alonso. « Métodos de control en sistemas domóticos : últimas tendencias en sistemas distribuidos ». Informes de la Construcción 50, n^o 459 (28 février 1999) : 43–53. http://dx.doi.org/10.3989/ic.1999.v50.i459.830.

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7

Ham, Won K., Yongho Chung et Sang C. Park. « Distributed System Design for the Control and Evaluation of Engagement Simulations ». International Journal of Modeling and Optimization 4, n^o 3 (juin 2014) : 171–75. http://dx.doi.org/10.7763/ijmo.2014.v4.368.

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8

LUCHIAN, Andrei-Mihai, et Mircea BOȘCOIANU. « DISTRIBUTED COMMUNICATION AND CONTROL FOR MULTIAGENT SYSTEMS : MICROINDUSTRIAL VEHICLE ROTORS (MAV) ». SCIENTIFIC RESEARCH AND EDUCATION IN THE AIR FORCE 20 (18 juin 2018) : 197–202. http://dx.doi.org/10.19062/2247-3173.2018.20.25.

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9

Borkute, Ruchali, et Nikita Malwar. « Control for Grid Connected and Intentional Islanding of Distributed Power Generation ». International Journal of Trend in Scientific Research and Development Volume-3, Issue-4 (30 juin 2019) : 333–36. http://dx.doi.org/10.31142/ijtsrd23679.

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10

Prince, Daryl. « Distributed Control ». Mechanical Engineering 121, n^o 01 (1 janvier 1999) : 68–69. http://dx.doi.org/10.1115/1.1999-jan-6.

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This article discusses servo motion systems, which are motion control systems that combine hardware and software, have innumerable applications in compact modules. Some motion controllers operate on multiple platforms and buses, with units providing analog output to a conventional amplifier, as well as units that provide current control and direct pulse width modulation (PWM) output for as many as 32 motors simultaneously. There are amplifiers that still require potentiometers to be adjusted for the digital drives’ position, velocity, and current control. All major value-adding components of motion control systems will soon have to comply with the demands for faster controllers with high-speed multi axis capabilities supplying commands in multitasking applications.

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11

Bors, D., et S. Walczak. « Optimal control elliptic systems with distributed and boundary controls ». Nonlinear Analysis : Theory, Methods & ; Applications 63, n^o 5-7 (novembre 2005) : e1367-e1376. http://dx.doi.org/10.1016/j.na.2005.02.009.

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12

Mansimov, K. B. « Singular controls in control problems for distributed-parameter systems ». Journal of Mathematical Sciences 148, n^o 3 (janvier 2008) : 331–81. http://dx.doi.org/10.1007/s10958-008-0009-0.

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13

Marden, Jason R., et Jeff S. Shamma. « Game Theory and Control ». Annual Review of Control, Robotics, and Autonomous Systems 1, n^o 1 (28 mai 2018) : 105–34. http://dx.doi.org/10.1146/annurev-control-060117-105102.

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Game theory is the study of decision problems in which there are multiple decision makers and the quality of a decision maker's choice depends on both that choice and the choices of others. While game theory has been studied predominantly as a modeling paradigm in the mathematical social sciences, there is a strong connection to control systems in that a controller can be viewed as a decision-making entity. Accordingly, game theory is relevant in settings with multiple interacting controllers. This article presents an introduction to game theory, followed by a sampling of results in three specific control theory topics where game theory has played a significant role: ( a) zero-sum games, in which the two competing players are a controller and an adversarial environment; ( b) team games, in which several controllers pursue a common goal but have access to different information; and ( c) distributed control, in which both a game and online adaptive rules are designed to enable distributed interacting subsystems to achieve a collective objective.

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14

Mukhopadhyay, Snehasis. « Distributed control and distributed computing ». ACM SIGAPP Applied Computing Review 7, n^o 1 (avril 1999) : 23–24. http://dx.doi.org/10.1145/570150.570157.

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15

Ko, Jea-Ho, et Chang-Soo Ok. « Advanced Distributed Arrival Time Control for Single Machine Problem in Dynamic Scheduling Environment ». Journal of Korean Institute of Industrial Engineers 38, n^o 1 (1 mars 2012) : 31–40. http://dx.doi.org/10.7232/jkiie.2012.38.1.031.

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16

Yasuda, Gen'ichi. « Petri Net Model Based Specification and Distributed Control of Robotic Manufacturing Systems ». Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010) : 410–15. http://dx.doi.org/10.1299/jsmeicam.2010.5.410.

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17

M.S, Brindha. « A Survey on Cross Layer Distributed Topology Control in Mobile Adhoc Network ». Bonfring International Journal of Networking Technologies and Applications 04, n^o 01 (31 octobre 2017) : 01–03. http://dx.doi.org/10.9756/bijnta.8346.

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18

Kaya, A., A. Kumar et J. Glass. « Instrumentation, Control and Management of Batch Reactors Using Distributed Controls ». IFAC Proceedings Volumes 26, n^o 2 (juillet 1993) : 629–32. http://dx.doi.org/10.1016/s1474-6670(17)48343-1.

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19

Reed, Morton W. « Distributed simulation using distributed control systems ». ACM SIGSIM Simulation Digest 20, n^o 4 (avril 1990) : 143–51. http://dx.doi.org/10.1145/99637.99656.

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20

Botchkaryov, A. « METHOD FOR DECENTRALIZED CONTROL OF ADAPTIVE DATA COLLECTION PROCESSES IN AUTONOMOUS DISTRIBUTED SYSTEMS ». Computer systems and network 5, n^o 1 (16 décembre 2023) : 8–19. http://dx.doi.org/10.23939/csn2023.01.008.

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The problem of monitoring a computer network under conditions of limitations on the use of system resources and high requirements for the survivability of the monitoring system has been considered. An autonomous decentralized computer network monitoring system has been developed, consisting of a team of software agents. Each agent can operate in two modes: main mode and monitoring system management console mode. In the main mode, the agent collects information about the computer network. In management console mode, the agent provides the user with access to information collected by all agents and allows the user to execute commands to manage the monitoring system. The developed monitoring system allows you to obtain more reliable information about the operation of the network with greater efficiency under the conditions of limitations on the use of system resources specified by the user. The autonomous monitoring system is created on the basis of the concept of multi-agent systems, within which a software agent of the system has some initiative for planning and implementing monitoring scenarios. The operation of software agents implements methods for organizing adaptive processes for collecting information using the principles of self-organization and the concept of structural adaptation. A decentralized software architecture for an autonomous monitoring system without a control center has been proposed. This ensures high reliability and survivability of the monitoring system. The software architecture of the autonomous monitoring system implements the SMA application software interface and the corresponding software library, which allows you to collect statistical data on the operation of the computer network and its nodes. The implementation of a software agent and a management console for an autonomous computer network monitoring system has been considered. Key words: computer network monitoring, autonomous system, decentralized control, software agent

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21

Pauly, Thomas. « Distributed control systems ». Electronics and Power 33, n^o 9 (1987) : 573. http://dx.doi.org/10.1049/ep.1987.0351.

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22

Biswas, Debmalya, Nikolai Nefedov et Valtteri Niemi. « Distributed Usage Control ». Procedia Computer Science 5 (2011) : 562–69. http://dx.doi.org/10.1016/j.procs.2011.07.073.

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23

Pretschner, Alexander, Manuel Hilty et David Basin. « Distributed usage control ». Communications of the ACM 49, n^o 9 (septembre 2006) : 39–44. http://dx.doi.org/10.1145/1151030.1151053.

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24

Kelly, F. P., P. B. Key et S. Zachary. « Distributed admission control ». IEEE Journal on Selected Areas in Communications 18, n^o 12 (décembre 2000) : 2617–28. http://dx.doi.org/10.1109/49.898741.

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25

Gomes, L., et A. Steiger-Garção. « Sistema distribuido para monitorización y control integrado de edificios ». Informes de la Construcción 50, n^o 459 (28 février 1999) : 35–42. http://dx.doi.org/10.3989/ic.1999.v50.i459.829.

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26

Ma, Chao-Tsung, et Tzung-Han Shr. « Power Flow Control of Renewable Energy Based Distributed Generators Using Advanced Power Converter Technologies ». Journal of Clean Energy Technologies 3, n^o 1 (2015) : 48–53. http://dx.doi.org/10.7763/jocet.2015.v3.167.

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27

Morkun, Volodymyr, Oleksandr Savytskyi et Maxym Tymoshenko. « Multiagent Control and Predictive Diagnostics of Distributed Iron Ore Enrichment System Based on CPS ». Advances in Cyber-Physical Systems 1, n^o 2 (23 février 2016) : 119–24. http://dx.doi.org/10.23939/acps2016.02.119.

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28

Lu, Zhigang, et Baoxu Liu. « ICONE19-43779 Safety and Security Analysis for Distributed Control System in Nuclear Power Plants ». Proceedings of the International Conference on Nuclear Engineering (ICONE) 2011.19 (2011) : _ICONE1943. http://dx.doi.org/10.1299/jsmeicone.2011.19._icone1943_303.

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29

Kaya, A., et M. A. Keyes. « Parameter Estimation and Optimal Control of Polyethylene Reactor Using Distributed Controls ». IFAC Proceedings Volumes 25, n^o 15 (juillet 1992) : 591–95. http://dx.doi.org/10.1016/s1474-6670(17)50697-7.

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30

Bartal, Yair, et Adi Rosén. « The Distributedk-Server Problem—A Competitive Distributed Translator fork-Server Algorithms ». Journal of Algorithms 23, n^o 2 (mai 1997) : 241–64. http://dx.doi.org/10.1006/jagm.1996.0826.

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31

Kader, Mahamane, Michel Lenczner et Zeljko Mrcarica. « Distributed control based on distributed electronic circuits : application to vibration control ». Microelectronics Reliability 41, n^o 11 (novembre 2001) : 1857–66. http://dx.doi.org/10.1016/s0026-2714(01)00038-5.

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32

Demidenko, Nikolai D., et Lyudmila V. Kulagina. « Distributed Control for Systems with Distributed Parametres ». Journal of Siberian Federal University. Engineering & ; Technologies 11, n^o 2 (mars 2018) : 221–28. http://dx.doi.org/10.17516/1999-494x-0025.

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33

Bamieh, Bassam, et Petros Voulgaris. « OPTIMAL DISTRIBUTED CONTROL WITH DISTRIBUTED DELAYED MEASUREMENTS ». IFAC Proceedings Volumes 35, n^o 1 (2002) : 95–100. http://dx.doi.org/10.3182/20020721-6-es-1901.00584.

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34

Schoop, Ronald, et Heinz-Dieter Ferling. « Control Blocks for Distributed Control Systems ». IFAC Proceedings Volumes 30, n^o 15 (juillet 1997) : 125–30. http://dx.doi.org/10.1016/s1474-6670(17)42678-4.

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35

Sorrell, Ethan, Michael E. Rule et Timothy O'Leary. « Brain–Machine Interfaces : Closed-Loop Control in an Adaptive System ». Annual Review of Control, Robotics, and Autonomous Systems 4, n^o 1 (3 mai 2021) : 167–89. http://dx.doi.org/10.1146/annurev-control-061720-012348.

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Brain–machine interfaces (BMIs) promise to restore movement and communication in people with paralysis and ultimately allow the human brain to interact seamlessly with external devices, paving the way for a new wave of medical and consumer technology. However, neural activity can adapt and change over time, presenting a substantial challenge for reliable BMI implementation. Large-scale recordings in animal studies now allow us to study how behavioral information is distributed in multiple brain areas, and state-of-the-art interfaces now incorporate models of the brain as a feedback controller. Ongoing research aims to understand the impact of neural plasticity on BMIs and find ways to leverage learning while accommodating unexpected changes in the neural code. We review the current state of experimental and clinical BMI research, focusing on what we know about the neural code, methods for optimizing decoders for closed-loop control, and emerging strategies for addressing neural plasticity.

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36

Jin, Wanxin, et Shaoshuai Mou. « Distributed inverse optimal control ». Automatica 129 (juillet 2021) : 109658. http://dx.doi.org/10.1016/j.automatica.2021.109658.

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37

Peled, Doron, et Sven Schewe. « Practical Distributed Control Synthesis ». Electronic Proceedings in Theoretical Computer Science 73 (11 novembre 2011) : 2–17. http://dx.doi.org/10.4204/eptcs.73.2.

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38

Awad, B., J. Wu et N. Jenkins. « Control of distributed generation ». e & ; i Elektrotechnik und Informationstechnik 125, n^o 12 (décembre 2008) : 409–14. http://dx.doi.org/10.1007/s00502-008-0591-3.

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39

Elmqvist, H. « Cooperating Distributed Control Objects ». IFAC Proceedings Volumes 24, n^o 5 (août 1991) : 119–24. http://dx.doi.org/10.1016/s1474-6670(17)51234-3.

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40

Thomas, André, Damien Trentesaux et Paul Valckenaers. « Intelligent distributed production control ». Journal of Intelligent Manufacturing 23, n^o 6 (10 novembre 2011) : 2507–12. http://dx.doi.org/10.1007/s10845-011-0601-x.

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41

Dummermuth, E. H. « Distributed real-time control ». IFAC Proceedings Volumes 18, n^o 1 (mai 1985) : 63–67. http://dx.doi.org/10.1016/b978-0-08-031664-2.50016-4.

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42

Kouthon, T., G. Noubir, P. Raja, Christopher P. Fuhrman et J. D. Decotignie. « Modeling Distributed PLC Control ». IFAC Proceedings Volumes 28, n^o 5 (mai 1995) : 215–22. http://dx.doi.org/10.1016/s1474-6670(17)47231-4.

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43

Khalgui, Mohamed, et Olfa Mosbahi. « Intelligent distributed control systems ». Information and Software Technology 52, n^o 12 (décembre 2010) : 1259–71. http://dx.doi.org/10.1016/j.infsof.2010.06.001.

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44

Draper, David. « Fully distributed batch control ». ISA Transactions 28, n^o 3 (janvier 1989) : 17–23. http://dx.doi.org/10.1016/0019-0578(89)90022-0.

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45

Andersen, John P. « Distributed control system testing ». ISA Transactions 30, n^o 2 (janvier 1991) : 41–45. http://dx.doi.org/10.1016/0019-0578(91)90038-7.

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46

Camacho, Eduardo F., et Carlos Bordons. « Distributed model predictive control ». Optimal Control Applications and Methods 36, n^o 3 (20 mars 2015) : 269–71. http://dx.doi.org/10.1002/oca.2167.

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47

Kharitonov, A., et O. Sawodny. « Flatness-based feedforward control for parabolic distributed parameter systems with distributed control ». International Journal of Control 79, n^o 7 (juillet 2006) : 677–87. http://dx.doi.org/10.1080/00207170600622858.

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48

Zeng, Jing, et Jinfeng Liu. « Distributed State Estimation Based Distributed Model Predictive Control ». Mathematics 9, n^o 12 (9 juin 2021) : 1327. http://dx.doi.org/10.3390/math9121327.

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In this work, we consider output-feedback distributed model predictive control (DMPC) based on distributed state estimation with bounded process disturbances and output measurement noise. Specifically, a state estimation scheme based on observer-enhanced distributed moving horizon estimation (DMHE) is considered for distributed state estimation purposes. The observer-enhanced DMHE ensures that the state estimates of the system reach a small neighborhood of the actual state values quickly and then maintain within the neighborhood. This implies that the estimation error is bounded. Based on the state estimates provided by the DMHE, a DMPC algorithm is developed based on Lyapunov techniques. In the proposed design, the DMHE and the DMPC are evaluated synchronously every sampling time. The proposed output DMPC is applied to a simulated chemical process and the simulation results show the applicability and effectiveness of the proposed distributed estimation and control approach.

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49

MINAMI, Yuki, et Takateru KOSAKA. « 1101 Distributed cooperative control of distributed generation systems ». Proceedings of the Optimization Symposium 2012.10 (2012) : _1101–1_—_1101–4_. http://dx.doi.org/10.1299/jsmeopt.2012.10.0__1101-1_.

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50

Di Blasio, G. « Optimal Control with Infinite Horizon for Distributed Parameter Systems with Constrained Controls ». SIAM Journal on Control and Optimization 29, n^o 4 (juillet 1991) : 909–25. http://dx.doi.org/10.1137/0329050.

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