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Journal articles on the topic 'Control over networks'

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Author: Grafiati

Published: 4 June 2021

Last updated: 31 January 2023

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1

Onat, Ahmet, Teoman Naskali, Emrah Parlakay, and Ozan Mutluer. "Control Over Imperfect Networks: Model-Based Predictive Networked Control Systems." IEEE Transactions on Industrial Electronics 58, no. 3 (March 2011): 905–13. http://dx.doi.org/10.1109/tie.2010.2051932.

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2

Di Benedetto, Maria Domenica, Karl Henrik Johansson, Mikael Johansson, and Fortunato Santucci. "Industrial control over wireless networks." International Journal of Robust and Nonlinear Control 20, no. 2 (January 25, 2010): 119–22. http://dx.doi.org/10.1002/rnc.1562.

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3

Pajic, Miroslav, Shreyas Sundaram, George J. Pappas, and Rahul Mangharam. "The Wireless Control Network: A New Approach for Control Over Networks." IEEE Transactions on Automatic Control 56, no. 10 (October 2011): 2305–18. http://dx.doi.org/10.1109/tac.2011.2163864.

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4

Doerfel, Marya L., Yannick Atouba, and Jack L. Harris. "(Un)Obtrusive Control in Emergent Networks: Examining Funding Agencies’ Control Over Nonprofit Networks." Nonprofit and Voluntary Sector Quarterly 46, no. 3 (August 21, 2016): 469–87. http://dx.doi.org/10.1177/0899764016664588.

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Nonprofit sector organizations tackle intractable problems by seeking support from external funding agencies, resulting in funders holding power through resource control. Nonprofits also access resources and coordinate activities through building networks with other nonprofits. Such networks have been viewed as emergent with an underlying assumption that the nonprofits determine when and with whom to partner. Given the power of funders, however, how much control do the nonprofits have in determining whether or not to partner? Document analysis of 83 application packets used by funders in the United States to collect and assess nonprofit suitability for funding shows significant differences between private- and public-sector control over nonprofits decisions to network. Unlike private-sector foundations, public-agency funding documents mandate awardees to network, which has practical and theoretical implications. Although the idea of building a network implies autonomous acts on the part of nonprofits, some are prone to hierarchical influences through grant-making policy.

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5

Chen, Jiming, and David K. Y. Yau. "Control and optimization over wireless networks." Journal of Network and Computer Applications 34, no. 6 (November 2011): 1771–72. http://dx.doi.org/10.1016/j.jnca.2011.07.002.

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6

Nedić, Angelia, and Ji Liu. "Distributed Optimization for Control." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 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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7

Savla, Ketan, Jeff S. Shamma, and Munther A. Dahleh. "Network Effects on the Robustness of Dynamic Systems." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 115–49. http://dx.doi.org/10.1146/annurev-control-091219-012549.

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We review selected results related to the robustness of networked systems in finite and asymptotically large size regimes in static and dynamical settings. In the static setting, within the framework of flow over finite networks, we discuss the effect of physical constraints on robustness to loss in link capacities. In the dynamical setting, we review several settings in which small-gain-type analysis provides tight robustness guarantees for linear dynamics over finite networks toward worst-case and stochastic disturbances. We discuss network flow dynamic settings where nonlinear techniques facilitate understanding the effect, on robustness, of constraints on capacity and information, substituting information with control action, and cascading failure. We also contrast cascading failure with a representative contagion model. For asymptotically large networks, we discuss the role of network properties in connecting microscopic shocks to emergent macroscopic fluctuations under linear dynamics as well as for economic networks at equilibrium. Through this review, we aim to achieve two objectives: to highlight selected settings in which the role of the interconnectivity structure of a network in its robustness is well understood, and to highlight a few additional settings in which existing system-theoretic tools give tight robustness guarantees and that are also appropriate avenues for future network-theoretic investigations.

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8

Pan, Ya-Jun, Peter X. Liu, Yang Shi, and Jie Sheng. "Advances in Methods for Control over Networks." Journal of Control Science and Engineering 2012 (2012): 1–2. http://dx.doi.org/10.1155/2012/136809.

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9

Lee, H. J., and J. T. Lim. "Fair congestion control over wireless multihop networks." IET Communications 6, no. 11 (2012): 1475. http://dx.doi.org/10.1049/iet-com.2010.0658.

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10

Ferrer, M., M. de Diego, G. Piñero, and A. Gonzalez. "Active noise control over adaptive distributed networks." Signal Processing 107 (February 2015): 82–95. http://dx.doi.org/10.1016/j.sigpro.2014.07.026.

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11

Lelarge, Marc. "Efficient control of epidemics over random networks." ACM SIGMETRICS Performance Evaluation Review 37, no. 1 (June 15, 2009): 1–12. http://dx.doi.org/10.1145/2492101.1555351.

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12

Tavassoli, B., P. Jabehdar-Maralani, and N. Rezaee. "Tuning of Control Systems Over CSMA Networks." IEEE Transactions on Industrial Electronics 56, no. 4 (April 2009): 1282–91. http://dx.doi.org/10.1109/tie.2008.2007553.

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13

Liu, Qinglong, and Gang Feng. "Joint Transmission Rate Control and Opportunistic Network Coding Over Wireless Networks." Wireless Personal Communications 78, no. 1 (April 3, 2014): 119–36. http://dx.doi.org/10.1007/s11277-014-1739-6.

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14

Das, A. K., M. Mesbahi, and Y. Hatano. "Agreement over noisy networks." IET Control Theory & Applications 4, no. 11 (November 1, 2010): 2416–26. http://dx.doi.org/10.1049/iet-cta.2009.0394.

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15

Koufogiannis, Fragkiskos, and George J. Pappas. "Diffusing Private Data Over Networks." IEEE Transactions on Control of Network Systems 5, no. 3 (September 2018): 1027–37. http://dx.doi.org/10.1109/tcns.2017.2673414.

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16

Zino, Lorenzo, Alessandro Rizzo, and Maurizio Porfiri. "Consensus Over Activity-Driven Networks." IEEE Transactions on Control of Network Systems 7, no. 2 (June 2020): 866–77. http://dx.doi.org/10.1109/tcns.2019.2949387.

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17

Iftar, Altug. "DECENTRALIZED ROBUST CONTROL OF SYSTEMS INTERCONNECTED OVER NETWORKS." IFAC Proceedings Volumes 46, no. 13 (2013): 37–42. http://dx.doi.org/10.3182/20130708-3-cn-2036.00017.

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18

LIU, David Q., and Williana Jean BAPTISTE. "On Approaches to Congestion Control over Wireless Networks." International Journal of Communications, Network and System Sciences 02, no. 03 (2009): 222–28. http://dx.doi.org/10.4236/ijcns.2009.23024.

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19

ISHII, Hideaki, and Koji TSUMURA. "Data Rate Limitations in Feedback Control over Networks." IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences E95-A, no. 4 (2012): 680–90. http://dx.doi.org/10.1587/transfun.e95.a.680.

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20

Schenato, Luca, Bruno Sinopoli, Massimo Franceschetti, Kameshwar Poolla, and S. Shankar Sastry. "Foundations of Control and Estimation Over Lossy Networks." Proceedings of the IEEE 95, no. 1 (January 2007): 163–87. http://dx.doi.org/10.1109/jproc.2006.887306.

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21

Gupta, V., A. F. Dana, J. P. Hespanha, R. M. Murray, and B. Hassibi. "Data Transmission Over Networks for Estimation and Control." IEEE Transactions on Automatic Control 54, no. 8 (August 2009): 1807–19. http://dx.doi.org/10.1109/tac.2009.2024567.

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22

Elia, Nicola, and Jeff N. Eisenbeis. "Limitations of Linear Control Over Packet Drop Networks." IEEE Transactions on Automatic Control 56, no. 4 (April 2011): 826–41. http://dx.doi.org/10.1109/tac.2010.2080150.

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23

Cuenca, Ángel, Ricardo Pizá, Julián Salt, and Antonio Sala. "Linear Matrix Inequalities in Multirate Control over Networks." Mathematical Problems in Engineering 2012 (2012): 1–22. http://dx.doi.org/10.1155/2012/768212.

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This paper faces two of the main drawbacks in networked control systems: bandwidth constraints and timevarying delays. The bandwidth limitations are solved by using multirate control techniques. The resultant multirate controller must ensure closed-loop stability in the presence of time-varying delays. Some stability conditions and a state feedback controller design are formulated in terms of linear matrix inequalities. The theoretical proposal is validated in two different experimental environments: a crane-based test-bed over Ethernet, and a maglev based platform over Profibus.

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24

Pogodaev, Aleksandr A., Albert S. Y. Wong, and Wilhelm T. S. Huck. "Photochemical Control over Oscillations in Chemical Reaction Networks." Journal of the American Chemical Society 139, no. 43 (October 19, 2017): 15296–99. http://dx.doi.org/10.1021/jacs.7b08109.

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25

El-Ocla, Hosam. "TCP CERL: congestion control enhancement over wireless networks." Wireless Networks 16, no. 1 (June 24, 2008): 183–98. http://dx.doi.org/10.1007/s11276-008-0123-4.

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26

Fischer, Jörg, Maxim Dolgov, and Uwe D. Hanebeck. "Optimal Sequence-Based Tracking Control over Unreliable Networks." IFAC Proceedings Volumes 47, no. 3 (2014): 3776–83. http://dx.doi.org/10.3182/20140824-6-za-1003.02572.

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27

Muradore, Riccardo, Davide Quaglia, and Paolo Fiorini. "Adaptive LQ Control over Differentiated Service Lossy Networks." IFAC Proceedings Volumes 44, no. 1 (January 2011): 13245–50. http://dx.doi.org/10.3182/20110828-6-it-1002.03484.

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28

Chen, Pin-Yu, Shin-Ming Cheng, and Kwang-Cheng Chen. "Optimal Control of Epidemic Information Dissemination Over Networks." IEEE Transactions on Cybernetics 44, no. 12 (December 2014): 2316–28. http://dx.doi.org/10.1109/tcyb.2014.2306781.

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29

Wenjun Luo and M. El Zarki. "Quality control for VBR video over ATM networks." IEEE Journal on Selected Areas in Communications 15, no. 6 (1997): 1029–39. http://dx.doi.org/10.1109/49.611157.

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30

Lombardo, A., S. Palazzo, and D. Panno. "Admission control over mixed traffic in ATM networks." International Journal of Digital & Analog Communication Systems 3, no. 2 (April 1990): 155–59. http://dx.doi.org/10.1002/dac.4510030210.

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31

Dongmei, Zhang, and Wang Xingang. "Observer-based H∞ control over packet dropping networks." Journal of Systems Engineering and Electronics 19, no. 6 (December 2008): 1215–25. http://dx.doi.org/10.1016/s1004-4132(08)60222-4.

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32

Jiang, Shengxiang, Petros G. Voulgaris, and Natasha Neogi. "Distributed control over structured and packet-dropping networks." International Journal of Robust and Nonlinear Control 18, no. 14 (September 25, 2008): 1389–408. http://dx.doi.org/10.1002/rnc.1284.

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33

Kim, Jae-Hoon, Seungchul Lee, and Sengphil Hong. "Autonomous Operation Control of IoT Blockchain Networks." Electronics 10, no. 2 (January 17, 2021): 204. http://dx.doi.org/10.3390/electronics10020204.

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Internet of Things (IoT) networks are typically composed of many sensors and actuators. The operation controls for robots in smart factories or drones produce a massive volume of data that requires high reliability. A blockchain architecture can be used to build highly reliable IoT networks. The shared ledger and open data validation among users guarantee extremely high data security. However, current blockchain technology has limitations for its overall application across IoT networks. Because general permission-less blockchain networks typically target high-performance network nodes with sufficient computing power, a blockchain node with low computing power and memory, such as an IoT sensor/actuator, cannot operate in a blockchain as a fully functional node. A lightweight blockchain provides practical blockchain availability over IoT networks. We propose essential operational advances to develop a lightweight blockchain over IoT networks. A dynamic network configuration enforced by deep clustering provides ad-hoc flexibility for IoT network environments. The proposed graph neural network technique enhances the efficiency of dApp (distributed application) spreading across IoT networks. In addition, the proposed blockchain technology is highly implementable in software because it adopts the Hyperledger development environment. Directly embedding the proposed blockchain middleware platform in small computing devices proves the practicability of the proposed methods.

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34

Nwazor, Nkolika Ogechukwu, and Eliezar Elisha Audu. "Data communications network for real-time industrial control systems." Nigerian Journal of Technological Development 19, no. 1 (June 6, 2022): 48–58. http://dx.doi.org/10.4314/njtd.v19i1.6.

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The advancements in network technologies and the evolution of the Internet of Things (IoT) have made supporting industrial control systems over probabilistic data networks promising. However, control systems’ communication over the traditional data networks is faced with problems of instability in feedback control and poor quality of performance due to time-varying data propagation delay. This paper presents two approaches that can enable real-time industrial control over non-deterministic computer networks allowing control system designers to take advantage of the existing communication infrastructures. The first approach is based on system-level interaction over two wires network called the collaboration network. The second approach is based on the implementation of the virtual local area network (VLAN). This method allows real-time control of industrial equipment or systems over IP-based networks while other computers are connected. Nodes providing real-time control services have the same PortID on the VLAN switch. This approach minimizes data traffic and reduces time-varying delay in system control over IP networks. The first approach was modeled and simulated using Proteus ISIS software. Two PIC16F877A microcontrollers were used to represent two nodes. CISCO packet tracer was used in the second approach to model and simulate IP-based control system communications over the traditional data network. Results indicate that the use of a two-wire collaborative network approach to a real-time control system is effective but requires an additional network alongside the main data traffic channel. VLAN, therefore, presents a more flexible approach that relies on the same infrastructures.

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35

Chen, Phoebus, Chithrupa Ramesh, and Karl H. Johansson. "Network Estimation and Packet Delivery Prediction for Control over Wireless Mesh Networks." IFAC Proceedings Volumes 44, no. 1 (January 2011): 6573–79. http://dx.doi.org/10.3182/20110828-6-it-1002.00828.

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36

Wang, Jing, and Nicola Elia. "Consensus over networks with dynamic channels." International Journal of Systems, Control and Communications 2, no. 1/2/3 (2010): 275. http://dx.doi.org/10.1504/ijscc.2010.031167.

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37

Pezzutto, M., M. Farina, R. Carli, and L. Schenato. "Remote MPC for Tracking Over Lossy Networks." IEEE Control Systems Letters 6 (2022): 1040–45. http://dx.doi.org/10.1109/lcsys.2021.3088749.

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38

Hee-Jung Byun and Jong-Tae Lim. "Rate-based feedback control over TCP wireless networks using supervisory control." IEEE Communications Letters 9, no. 7 (July 2005): 610–12. http://dx.doi.org/10.1109/lcomm.2005.1461680.

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39

Yi, Hyun-Chul, Cheol-Jin An, and Joon-Young Choi. "Compensation of Time-Varying Delay in Networked Control System over Wi-Fi Network." International Journal of Computers Communications & Control 12, no. 3 (April 23, 2017): 415. http://dx.doi.org/10.15837/ijccc.2017.3.2617.

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In this study, we design a state predictor-based output feedback controller that compensates for unavoidable time-varying network delays in networked control systems (NCSs) over Wi-Fi networks. We model time-varying network delays as timevarying input delays of NCSs over Wi-Fi networks. The designed controller consists of a linear quadratic regulator (LQR), a full-order observer, and a time-varying stepahead state predictor. The state predictor plays a key role in compensating for the time-varying input delay by providing the LQR with an estimation of future states ahead by the current network delay time. The time-varying network delays are acquired in real time by measuring the time differences between sent and received control data packets. We verify the stability and compensation performance of the designed controller by performing extensive experiments for an NCS in which a rotary inverted pendulum is controlled over Wi-Fi networks.

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40

Wang, Ping, Lu Wei Chen, Mei Song, and Ying Hai Zang. "Cross-Layer QoSArchitecture for Mobility Management over Heterogeneous Wireless Networks." Advanced Materials Research 433-440 (January 2012): 5058–62. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.5058.

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The development of wireless networks brings people great convenience. More state-of-the-art communication protocols of wireless networks are getting mature. People attach more importance to the QoS provisioning in heterogeneous wireless networks. This study proposes a cross-layer-based mobility management Quality-of-Service (QoS) architecture that includesthe QoS manager、the cross-layer scheme and the access control algorithm.In order to provide the QoS support for mobile user, the overall architecture for QoS management is required in heterogeneous wireless environment, where various mobile users are connected to the different access network through the core network. The QoS manager is composed of ANQM (Access Network QoS Manger) and IANQM (Inter-Access Network QoS Manger) which are used to provide consistent QoS management over an integrated wireless access network environment. The access control algorithm is based on the QoS mapping mechanism between the access network and the core network.The simulation result shows that the QoS for mobile user is guaranteed more effectively and this mechanism provides an end-to-end QoS between different access networks.

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41

Hatano, Y., and M. Mesbahi. "Agreement over random networks." IEEE Transactions on Automatic Control 50, no. 11 (November 2005): 1867–72. http://dx.doi.org/10.1109/tac.2005.858670.

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42

Chen, Yongxin, Tryphon Georgiou, Michele Pavon, and Allen Tannenbaum. "Robust Transport Over Networks." IEEE Transactions on Automatic Control 62, no. 9 (September 2017): 4675–82. http://dx.doi.org/10.1109/tac.2016.2626796.

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43

Reddy, N. Ramanjaneya, Chenna Reddy Pakanati, and M. Padmavathamma. "Performance Enhancement of TCP Friendly Rate Control Protocol over Wired networks." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (December 1, 2016): 2949. http://dx.doi.org/10.11591/ijece.v6i6.12560.

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<p>One of the main aims of transport layer protocol is achieving best throughput without any congestion or reduced congestion. With rapid growing application needs and with increasing number of networks in Internet, there is a primary need to design new protocols to transport layer. To transmit multimedia applications, one of the suitable congestion control mechanisms in transport layer is TCP Friendly Rate Control Protocol (TFRC). It controls congestion based on its equation. However, every packet requires an acknowledgement in TFRC. It creates congestion in the network when the transmitted data is very large, which results in reduced throughput. This paper aims to increase the throughput when the transmitted data is large with minimal congestion by reducing the number of acknowledgements in the network. We modified some fixed parameters in the TFRC equation. The results show the increased throughput with minimal congestion.</p>

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44

Reddy, N. Ramanjaneya, Chenna Reddy Pakanati, and M. Padmavathamma. "Performance Enhancement of TCP Friendly Rate Control Protocol over Wired networks." International Journal of Electrical and Computer Engineering (IJECE) 6, no. 6 (December 1, 2016): 2949. http://dx.doi.org/10.11591/ijece.v6i6.pp2949-2954.

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<p>One of the main aims of transport layer protocol is achieving best throughput without any congestion or reduced congestion. With rapid growing application needs and with increasing number of networks in Internet, there is a primary need to design new protocols to transport layer. To transmit multimedia applications, one of the suitable congestion control mechanisms in transport layer is TCP Friendly Rate Control Protocol (TFRC). It controls congestion based on its equation. However, every packet requires an acknowledgement in TFRC. It creates congestion in the network when the transmitted data is very large, which results in reduced throughput. This paper aims to increase the throughput when the transmitted data is large with minimal congestion by reducing the number of acknowledgements in the network. We modified some fixed parameters in the TFRC equation. The results show the increased throughput with minimal congestion.</p>

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45

Tiberi, U., C. Fischione, K. H. Johansson, and M. D. Di Benedetto. "Energy-efficient sampling of networked control systems over IEEE 802.15.4 wireless networks." Automatica 49, no. 3 (March 2013): 712–24. http://dx.doi.org/10.1016/j.automatica.2012.11.046.

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46

Pasqualetti, Fabio, Domenica Borra, and Francesco Bullo. "Consensus networks over finite fields." Automatica 50, no. 2 (February 2014): 349–58. http://dx.doi.org/10.1016/j.automatica.2013.11.011.

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47

Fu, Weiming, Jiahu Qin, Junfeng Wu, Wei Xing Zheng, and Yu Kang. "Interval consensus over random networks." Automatica 111 (January 2020): 108603. http://dx.doi.org/10.1016/j.automatica.2019.108603.

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48

Vissicchio, Stefano, Olivier Tilmans, Laurent Vanbever, and Jennifer Rexford. "Central Control Over Distributed Routing." ACM SIGCOMM Computer Communication Review 45, no. 4 (September 22, 2015): 43–56. http://dx.doi.org/10.1145/2829988.2787497.

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49

Du, Yan, Lijuan Jia, Shunshoku Kanae, and Zijiang Yang. "Diffusion logistic regression algorithms over multiagent networks." Control Theory and Technology 18, no. 2 (May 2020): 160–67. http://dx.doi.org/10.1007/s11768-020-0009-2.

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50

Sandhu, J., M. Mesbahi, and T. Tsukamaki. "On the Control and Estimation Over Relative Sensing Networks." IEEE Transactions on Automatic Control 54, no. 12 (December 2009): 2859–63. http://dx.doi.org/10.1109/tac.2009.2033137.

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