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Artículos de revistas sobre el tema "Control"

Autor: Grafiati

Publicado: 4 de junio de 2021

Última modificación: 25 de julio de 2025

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1

Gwon, Jun, Jong-Seok Kim, Young-Seok Lee, Oluleke Babayomi, and Ki-Bum Park. "A Model-Free Predictive Control for IPMSM Drive Based on Finite Control Set." TRANSACTIONS OF KOREAN INSTITUTE OF POWER ELECTRONICS 30, no. 2 (2025): 165–72. https://doi.org/10.6113/tkpe.2025.30.2.165.

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2

Abdulhadi, Sana, Muhammed M. Yakoub, and Ali S. Al- Nuaimi. "The Use of Simulated Acid Rain to Show its Effect on the Morphology of Vicia faba, Educe sativa and Spinacia oleracea and their Uptake of Iron and Potassium." Continuous Research Online Library 1, no. 1 (2017): 1–8. http://dx.doi.org/10.28915/control.0002.1.

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3

O. Khalifa, Massoudah, Salah H. Mohamed, Ibtisam M. Ahmadi, et al. "Effect of Hydrogen Ions and Aluminum Chloride on Isolated Rhizobia from Medicago sativa L." Continuous Research Online Library 1, no. 1 (2017): 1–11. http://dx.doi.org/10.28915/control.0003.1.

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4

Iskenderov, A. D., and R. K. Tagiyev. "OPTIMAL CONTROL PROBLEM WITH CONTROLS IN COEFFICIENTS OF QUASILINEAR ELLIPTIC EQUATION." Eurasian Journal of Mathematical and Computer Applications 1, no. 1 (2013): 21–38. http://dx.doi.org/10.32523/2306-3172-2013-1-2-21-38.

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5

Charo, R. Alta, Renate Klein, Janice Raymond, Lynette Dumble, Etienne-Emile Baulieu, and Mort Rosenblum. "Who Controls Birth Control?" Women's Review of Books 9, no. 9 (1992): 18. http://dx.doi.org/10.2307/4021278.

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6

Coll-Seck, Awa-Marie. "Who controls malaria control?" Nature 466, no. 7303 (2010): 186–87. http://dx.doi.org/10.1038/466186a.

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7

Seminara, Joseph L. "Taking Control of Controls." Ergonomics in Design: The Quarterly of Human Factors Applications 1, no. 3 (1993): 21–32. http://dx.doi.org/10.1177/106480469300100309.

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8

Schneider, Dirk. "Border controls: Lipids control proteins and proteins control lipids." Biochimica et Biophysica Acta (BBA) - Biomembranes 1859, no. 4 (2017): 507–8. http://dx.doi.org/10.1016/j.bbamem.2016.12.016.

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9

Hewing, Lukas, Kim P. Wabersich, Marcel Menner, and Melanie N. Zeilinger. "Learning-Based Model Predictive Control: Toward Safe Learning in Control." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 269–96. http://dx.doi.org/10.1146/annurev-control-090419-075625.

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Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Model predictive control (MPC), as the prime methodology for constrained control, offers a significant opportunity to exploit the abundance of data in a reliable manner, particularly while taking safety constraints into account. This review aims at summarizing and categorizing previous research on learning-based MPC, i.e., the integration or combination of MPC

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10

J, Lillykutty M., and Dr Rebecca Samson. "The Use of Historical Controls in Post-Test only Non-Equivalent Control Group." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (2018): 2605–14. http://dx.doi.org/10.31142/ijtsrd15653.

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11

James, M. R. "Optimal Quantum Control Theory." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (2021): 343–67. http://dx.doi.org/10.1146/annurev-control-061520-010444.

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This article explains some fundamental ideas concerning the optimal control of quantum systems through the study of a relatively simple two-level system coupled to optical fields. The model for this system includes both continuous and impulsive dynamics. Topics covered include open- and closed-loop control, impulsive control, open-loop optimal control, quantum filtering, and measurement feedback optimal control.

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12

Marden, Jason R., and Jeff S. Shamma. "Game Theory and Control." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (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 spec

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13

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

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14

KOLETZKO, SIBYLLE, NIKOLAOS KONSTANTOPOULOS, NORBERT LEHN, and DAVID FORMAN. "Control your controls and conclusions." Archives of Disease in Childhood 84, no. 6 (2001): 525.4–525. http://dx.doi.org/10.1136/adc.84.6.525-c.

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15

KOLETZKO, S. "Control your controls and conclusions." Archives of Disease in Childhood 84, no. 6 (2001): 525c—525. http://dx.doi.org/10.1136/adc.84.6.525c.

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16

Meshberg, Gary. "Controls: The Lighting Control Zone." Lighting Design + Application 42, no. 3 (2012): 20–22. https://doi.org/10.1177/036063251204200306.

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17

Patterson, Carole H. "Engineering controls vs. infection control." Nursing Management (Springhouse) 32 (June 2001): 29. http://dx.doi.org/10.1097/00006247-200106000-00018.

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18

Newcombe, Nora S. "Some Controls Control Too Much." Child Development 74, no. 4 (2003): 1050–52. http://dx.doi.org/10.1111/1467-8624.00588.

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19

Vaassen, E. H. J. "Control en de controllerfunctie." Maandblad Voor Accountancy en Bedrijfseconomie 77, no. 4 (2003): 146–54. http://dx.doi.org/10.5117/mab.77.16291.

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Management control en internal control zijn processen, ofwel onderling samenhangende activiteiten die ertoe moeten leiden dat organisaties ‘in control’ komen of blijven. De controller zal in meer of mindere mate steunen op management controls dan wel internal controls. De functie van controller wordt tegenwoordig op verschillende manieren ingevuld. Afhankelijk van de invulling die een bepaalde controller aan zijn functie geeft, zal hij in meer of mindere mate steunen op management control of internal control. De laatste decennia laten een ontwikkeling zien waarin de interne controle is opgesch

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20

Vaassen, E. H. J. "Control en de controllerfunctie." Maandblad Voor Accountancy en Bedrijfseconomie 77, no. (4) (2003): 146–54. https://doi.org/10.5117/mab.77.16291.

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Management control en internal control zijn processen, ofwel onderling samenhangende activiteiten die ertoe moeten leiden dat organisaties 'in control' komen of blijven. De controller zal in meer of mindere mate steunen op management controls dan wel internal controls. De functie van controller wordt tegenwoordig op verschillende manieren ingevuld. Afhankelijk van de invulling die een bepaalde controller aan zijn functie geeft, zal hij in meer of mindere mate steunen op management control of internal control. De laatste decennia laten een ontwikkeling zien waarin de interne controle is opgesch

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21

McNamee, Daniel, and Daniel M. Wolpert. "Internal Models in Biological Control." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (2019): 339–64. http://dx.doi.org/10.1146/annurev-control-060117-105206.

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Rationality principles such as optimal feedback control and Bayesian inference underpin a probabilistic framework that has accounted for a range of empirical phenomena in biological sensorimotor control. To facilitate the optimization of flexible and robust behaviors consistent with these theories, the ability to construct internal models of the motor system and environmental dynamics can be crucial. In the context of this theoretic formalism, we review the computational roles played by such internal models and the neural and behavioral evidence for their implementation in the brain.

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22

van der Schaft, Arjan. "Port-Hamiltonian Modeling for Control." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 393–416. http://dx.doi.org/10.1146/annurev-control-081219-092250.

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This article provides a concise summary of the basic ideas and concepts in port-Hamiltonian systems theory and its use in analysis and control of complex multiphysics systems. It gives special attention to new and unexplored research directions and relations with other mathematical frameworks. Emergent control paradigms and open problems are indicated, including the relation with thermodynamics and the question of uniting the energy-processing view of control, as emphasized by port-Hamiltonian systems theory, with a complementary information-processing viewpoint.

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23

Lu, Ping. "Tracking Control of Nonlinear Systems with Bounded Controls and Control Rates." IFAC Proceedings Volumes 29, no. 1 (1996): 2307–12. http://dx.doi.org/10.1016/s1474-6670(17)58017-9.

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24

Lu, Ping. "Tracking control of nonlinear systems with bounded controls and control rates." Automatica 33, no. 6 (1997): 1199–202. http://dx.doi.org/10.1016/s0005-1098(97)00033-2.

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25

Bock, Igor, and Ján Lovíšek. "Optimal control problems for variational inequalities with controls in coefficients and in unilateral constraints." Applications of Mathematics 32, no. 4 (1987): 301–14. http://dx.doi.org/10.21136/am.1987.104261.

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26

Goodwin, Matthew L. "Control, Control, Control; Where's Your Control?" Journal of the American College of Surgeons 215, no. 3 (2012): 445–46. http://dx.doi.org/10.1016/j.jamcollsurg.2012.06.013.

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27

Han, Shuo, and George J. Pappas. "Privacy in Control and Dynamical Systems." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (2018): 309–32. http://dx.doi.org/10.1146/annurev-control-060117-105018.

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Many modern dynamical systems, such as smart grids and traffic networks, rely on user data for efficient operation. These data often contain sensitive information that the participating users do not wish to reveal to the public. One major challenge is to protect the privacy of participating users when utilizing user data. Over the past decade, differential privacy has emerged as a mathematically rigorous approach that provides strong privacy guarantees. In particular, differential privacy has several useful properties, including resistance to both postprocessing and the use of side information

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28

Madhav, Manu S., and Noah J. Cowan. "The Synergy Between Neuroscience and Control Theory: The Nervous System as Inspiration for Hard Control Challenges." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (2020): 243–67. http://dx.doi.org/10.1146/annurev-control-060117-104856.

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Here, we review the role of control theory in modeling neural control systems through a top-down analysis approach. Specifically, we examine the role of the brain and central nervous system as the controller in the organism, connected to but isolated from the rest of the animal through insulated interfaces. Though biological and engineering control systems operate on similar principles, they differ in several critical features, which makes drawing inspiration from biology for engineering controllers challenging but worthwhile. We also outline a procedure that the control theorist can use to dr

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29

Gopalakrishnan, Karthik, and Hamsa Balakrishnan. "Control and Optimization of Air Traffic Networks." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (2021): 397–424. http://dx.doi.org/10.1146/annurev-control-070720-080844.

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The air transportation system connects the world through the transport of goods and people. However, operational inefficiencies such as flight delays and cancellations are prevalent, resulting in economic and environmental impacts. In the first part of this article, we review recent advances in using network analysis techniques to model the interdependencies observed in the air transportation system and to understand the role of airports in connecting populations, serving air traffic demand, and spreading delays. In the second part, we present some of our recent work on using operational data

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30

Lafortune, Stéphane. "Discrete Event Systems: Modeling, Observation, and Control." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (2019): 141–59. http://dx.doi.org/10.1146/annurev-control-053018-023659.

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This article begins with an introduction to the modeling of discrete event systems, a class of dynamical systems with discrete states and event-driven dynamics. It then focuses on logical discrete event models, primarily automata, and reviews observation and control problems and their solution methodologies. Specifically, it discusses diagnosability and opacity in the context of partially observed discrete event systems. It then discusses supervisory control for both fully and partially observed systems. The emphasis is on presenting fundamental results first, followed by a discussion of curre

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31

Rosolia, Ugo, Xiaojing Zhang, and Francesco Borrelli. "Data-Driven Predictive Control for Autonomous Systems." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (2018): 259–86. http://dx.doi.org/10.1146/annurev-control-060117-105215.

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In autonomous systems, the ability to make forecasts and cope with uncertain predictions is synonymous with intelligence. Model predictive control (MPC) is an established control methodology that systematically uses forecasts to compute real-time optimal control decisions. In MPC, at each time step an optimization problem is solved over a moving horizon. The objective is to find a control policy that minimizes a predicted performance index while satisfying operating constraints. Uncertainty in MPC is handled by optimizing over multiple uncertain forecasts. In this case, performance index and o

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32

Logan, John R., and Min Zhou. "Do Suburban Growth Controls Control Growth?" American Sociological Review 54, no. 3 (1989): 461. http://dx.doi.org/10.2307/2095617.

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33

Rivera, Diego Y. "Designing Soft Controls for Process Control." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 1 (2000): 120–23. http://dx.doi.org/10.1177/154193120004400132.

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34

Eletto, D., E. Chevet, Y. Argon, and C. Appenzeller-Herzog. "Redox controls UPR to control redox." Journal of Cell Science 127, no. 17 (2014): 3649–58. http://dx.doi.org/10.1242/jcs.153643.

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35

Lancaster, Tony, and Guido Imbens. "Case-control studies with contaminated controls." Journal of Econometrics 71, no. 1-2 (1996): 145–60. http://dx.doi.org/10.1016/0304-4076(94)01698-4.

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36

Hino, Junichi, Masao Kurimoto, and Motomichi Sonobe. "63103 Vibration Control of Truck Crane by Variable Constrained Control with Neural Network(Control of Multibody Systems)." Proceedings of the Asian Conference on Multibody Dynamics 2010.5 (2010): _63103–1_—_63103–8_. http://dx.doi.org/10.1299/jsmeacmd.2010.5._63103-1_.

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37

Goncharenko, Borys, Larysa Vikhrova, and Mariia Miroshnichenko. "Optimal control of nonlinear stationary systems at infinite control time." Central Ukrainian Scientific Bulletin. Technical Sciences, no. 4(35) (2021): 88–93. http://dx.doi.org/10.32515/2664-262x.2021.4(35).88-93.

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The article presents a solution to the problem of control synthesis for dynamical systems described by linear differential equations that function in accordance with the integral-quadratic quality criterion under uncertainty. External perturbations, errors and initial conditions belong to a certain set of uncertainties. Therefore, the problem of finding the optimal control in the form of feedback on the output of the object is presented in the form of a minimum problem of optimal control under uncertainty. The problem of finding the optimal control and initial state, which maximizes the qualit

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38

Sasaki, Tatsuya, and Nobuo Ogawa. "OPTICAL CONTROL OF JETS BY RADIATION PRESSURE(Flow Control 2)." Proceedings of the International Conference on Jets, Wakes and Separated Flows (ICJWSF) 2005 (2005): 483–88. http://dx.doi.org/10.1299/jsmeicjwsf.2005.483.

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39

Zhang, Haitao, and Zhen Li. "Fuzzy Immune Control Based Smith Predictor for Networked Control Systems." International Journal of Engineering and Technology 3, no. 1 (2011): 81–84. http://dx.doi.org/10.7763/ijet.2011.v3.204.

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40

Tašner, Tadej, and Darko Lovrec. "Maximum Efficiency Control – A New Strategy To Control Electrohydraulic Systems." Paripex - Indian Journal Of Research 3, no. 5 (2012): 107–9. http://dx.doi.org/10.15373/22501991/may2014/34.

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41

Sepulchre, R., G. Drion, and A. Franci. "Control Across Scales by Positive and Negative Feedback." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (2019): 89–113. http://dx.doi.org/10.1146/annurev-control-053018-023708.

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Feedback is a key element of regulation, as it shapes the sensitivity of a process to its environment. Positive feedback upregulates, and negative feedback downregulates. Many regulatory processes involve a mixture of both, whether in nature or in engineering. This article revisits the mixed-feedback paradigm, with the aim of investigating control across scales. We propose that mixed feedback regulates excitability and that excitability plays a central role in multiscale neuronal signaling. We analyze this role in a multiscale network architecture inspired by neurophysiology. The nodal behavio

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42

Belta, Calin, and Sadra Sadraddini. "Formal Methods for Control Synthesis: An Optimization Perspective." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (2019): 115–40. http://dx.doi.org/10.1146/annurev-control-053018-023717.

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In control theory, complicated dynamics such as systems of (nonlinear) differential equations are controlled mostly to achieve stability. This fundamental property, which can be with respect to a desired operating point or a prescribed trajectory, is often linked with optimality, which requires minimizing a certain cost along the trajectories of a stable system. In formal verification (model checking), simple systems, such as finite-state transition graphs that model computer programs or digital circuits, are checked against rich specifications given as formulas of temporal logics. The formal

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43

MARTINEZ FLORES, MIRNA MARICELA, María Aracelia Alcorta García, SANTOS MENDEZ DIAZ, JOSE ARMANDO SAENZ ESQUEDA, GERARDO MAXIMILIANO MENDEZ, and NORA ELIZONDO VILLAREAL. "TEMPERATURE CONTROL IN AN EVAPORATOR APPLYING RISK-SENSITIVE CONTROL." DYNA 97, no. 4 (2022): 345. http://dx.doi.org/10.6036/10498.

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This work designs a stochastic model of an evaporator of a refrigeration system, subject to specific conditions, applying the Risk-Sensitive (R-S) stochastic control equations with tracking, to control the evaporator temperature achieving great energy savings, where the actuator is the expansion valve (EEV).

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44

Elacqua, Elizabeth, Stephen J. Koehler, and Jinzhen Hu. "Electronically Governed ROMP: Expanding Sequence Control for Donor–Acceptor Conjugated Polymers." Synlett 31, no. 15 (2020): 1435–42. http://dx.doi.org/10.1055/s-0040-1707180.

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Controlling the primary sequence of synthetic polymers remains a grand challenge in chemistry. A variety of methods that exert control over monomer sequence have been realized wherein differential reactivity, pre-organization, and stimuli-response have been key factors in programming sequence. Whereas much has been established in nonconjugated systems, π-extended frameworks remain systems wherein subtle structural changes influence bulk properties. The recent introduction of electronically biased ring-opening metathesis polymerization (ROMP) extends the repertoire of feasible approaches to pre

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45

Chepuru, Anitha, Dr K. Venugopal Rao, and Amardeep Matta. "Server Access Control." International Journal of Scientific Research 1, no. 7 (2012): 78–79. http://dx.doi.org/10.15373/22778179/dec2012/31.

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46

Deka, Nayanmani, and Shahbaz Rahman. "Smart Vermicomposting Control." International Journal of Science and Research (IJSR) 11, no. 5 (2022): 737–38. http://dx.doi.org/10.21275/sr22509202320.

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47

Otto, Samuel E., and Clarence W. Rowley. "Koopman Operators for Estimation and Control of Dynamical Systems." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (2021): 59–87. http://dx.doi.org/10.1146/annurev-control-071020-010108.

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A common way to represent a system's dynamics is to specify how the state evolves in time. An alternative viewpoint is to specify how functions of the state evolve in time. This evolution of functions is governed by a linear operator called the Koopman operator, whose spectral properties reveal intrinsic features of a system. For instance, its eigenfunctions determine coordinates in which the dynamics evolve linearly. This review discusses the theoretical foundations of Koopman operator methods, as well as numerical methods developed over the past two decades to approximate the Koopman operato

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48

Hu, Bin, Kaiqing Zhang, Na Li, Mehran Mesbahi, Maryam Fazel, and Tamer Başar. "Toward a Theoretical Foundation of Policy Optimization for Learning Control Policies." Annual Review of Control, Robotics, and Autonomous Systems 6, no. 1 (2023): 123–58. http://dx.doi.org/10.1146/annurev-control-042920-020021.

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Gradient-based methods have been widely used for system design and optimization in diverse application domains. Recently, there has been a renewed interest in studying theoretical properties of these methods in the context of control and reinforcement learning. This article surveys some of the recent developments on policy optimization, a gradient-based iterative approach for feedback control synthesis that has been popularized by successes of reinforcement learning. We take an interdisciplinary perspective in our exposition that connects control theory, reinforcement learning, and large-scale

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49

Fligstein, Neil, and Peter Brantley. "Bank Control, Owner Control, or Organizational Dynamics: Who Controls the Large Modern Corporation?" American Journal of Sociology 98, no. 2 (1992): 280–307. http://dx.doi.org/10.1086/230009.

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50

Hamajima, Nobuyuki, Kaoru Hirose, Manami Inoue, Toshiro Takezaki, Testuo Kuroishi, and Kazuo Tajima. "Case-control studies: Matched controls or all available controls?" Journal of Clinical Epidemiology 47, no. 9 (1994): 971–75. http://dx.doi.org/10.1016/0895-4356(94)90111-2.

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