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Journal articles on the topic 'Control systems'

To see the other types of publications on this topic, follow the link: Control systems.

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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1

Pandolfi, L. "Generalized control systems, boundary control systems, and delayed control systems." Mathematics of Control, Signals, and Systems 3, no. 2 (June 1990): 165–81. http://dx.doi.org/10.1007/bf02551366.

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2

Irfan, C. M. Althaff, Karim Ouzzane, Shusaku Nomura, and Yoshimi Fukumura. "211 AN ACCESS CONTROL SYSTEM For E-Learning MANAGEMENT SYSTEMS." Proceedings of Conference of Hokuriku-Shinetsu Branch 2010.47 (2010): 59–60. http://dx.doi.org/10.1299/jsmehs.2010.47.59.

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3

Trofimov, A. M., and V. M. Moskovkin. "Optimal control over geomorphological systems." Zeitschrift für Geomorphologie 29, no. 3 (October 31, 1985): 257–63. http://dx.doi.org/10.1127/zfg/29/1985/257.

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4

Augusto Arbugeri, Cesar, Neilor Colombo Dal Pont, Tiago Kommers Jappe, Samir Ahmad Mussa, and Telles Brunelli Lazzarin. "Control System for Multi-Inverter Parallel Operation in Uninterruptible Power Systems." Eletrônica de Potência 24, no. 1 (February 1, 2018): 37–46. http://dx.doi.org/10.18618/rep.2019.1.0016.

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5

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 (May 3, 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 operator from data, for systems both with and without actuation. We pay special attention to ergodic systems, for which especially effective numerical methods are available. For nonlinear systems with an affine control input, the Koopman formalism leads naturally to systems that are bilinear in the state and the input, and this structure can be leveraged for the design of controllers and estimators.

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6

Han, Shuo, and George J. Pappas. "Privacy in Control and Dynamical Systems." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 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 by adversaries. Although differential privacy was first proposed for static-database applications, this review focuses on its use in the context of control systems, in which the data under processing often take the form of data streams. Through two major applications—filtering and optimization algorithms—we illustrate the use of mathematical tools from control and optimization to convert a nonprivate algorithm to its private counterpart. These tools also enable us to quantify the trade-offs between privacy and system performance.

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7

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 quality criterion, is considered in the framework of the optimization problem, which is solved by the method of Lagrange multipliers after the introduction of the auxiliary scalar function - Hamiltonian. The case of a stationary system on an infinite period of time is considered. The formulas that can be used for calculations are given for the first and second variations. It is proposed to solve the problem of control search in two stages: search of intermediate solution at fixed values of control and error vectors and subsequent search of final optimal control. The solution of -optimal control for infinite time taking into account the signal from the compensator output is also considered, as well as the solution of the corresponding matrix algebraic equations of Ricatti type.

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8

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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9

Tašner, Tadej, and Darko Lovrec. "Maximum Efficiency Control – A New Strategy To Control Electrohydraulic Systems." Paripex - Indian Journal Of Research 3, no. 5 (January 15, 2012): 107–9. http://dx.doi.org/10.15373/22501991/may2014/34.

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10

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

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11

Melnikov, Sergiy V., Sergiy O. Bondar, and Oleksiy Yu Gospodarchuk. "Modern Unmanned Aerial Vehicle Control Systems." Upravlâûŝie sistemy i mašiny, no. 6 (272) (January 2018): 84–90. http://dx.doi.org/10.15407/usim.2017.06.084.

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12

Tilbury, Dawn M. "Cyber-Physical Manufacturing Systems." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 2019): 427–43. http://dx.doi.org/10.1146/annurev-control-053018-023652.

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Cyber-physical systems, in which computation and networking technologies interact with physical systems, have made great strides into manufacturing systems. From the early days, when electromechanical relays were used to automate conveyors and machines, through the introduction of programmable logic controllers and computer numerical control, computing and networking have become pervasive in manufacturing systems. By increasing the amount of automation at multiple levels within a factory and across the enterprise, cyber-physical manufacturing systems enable higher productivity and higher quality as well as lower costs.

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13

Lafortune, Stéphane. "Discrete Event Systems: Modeling, Observation, and Control." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 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 current research directions.

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14

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 (May 28, 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 operating constraints take the form of functions defined over a probability space, and the resulting technique is called stochastic MPC. Our research over the past 10 years has focused on predictive control design methods that systematically handle uncertain forecasts in autonomous and semiautonomous systems. In the first part of this article, we present an overview of the approach we use, its main advantages, and its challenges. In the second part, we present our most recent results on data-driven predictive control. We show how to use data to efficiently formulate stochastic MPC problems and autonomously improve performance in repetitive tasks. The proposed framework is able to handle a large set of predicted scenarios in real time and learn from historical data.

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15

Alec Cram, W., M. Kathryn Brohman, Yolande E. Chan, and R. Brent Gallupe. "Information systems control alignment: Complementary and conflicting systems development controls." Information & Management 53, no. 2 (March 2016): 183–96. http://dx.doi.org/10.1016/j.im.2015.09.012.

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16

Javadi Moghaddam, Jalal, Ghasem Zarei, Davood Momeni, and Hamideh Faridi. "Non-linear control model for use in greenhouse climate control systems." Research in Agricultural Engineering 68, No. 1 (March 23, 2022): 9–17. http://dx.doi.org/10.17221/37/2021-rae.

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In this study, a non-linear control system was designed and proposed to control the greenhouse climate conditions. This control system directly uses the information of sensors, installed inside and outside the greenhouse. To design this proposed control system, the principles of a non-linear control system and the concepts of equilibrium points and zero dynamics of system theories were used. To show the capability and applicability of the proposed control system, it was compared with an integral sliding mode controller. A greenhouse with similar climatic conditions was used to simulate the performance of the integral sliding mode controller. In this study, it was seen that the integral sliding mode control system was more accurate; however, the actuator signals sent by this control system were not smooth. It could damage and depreciate the greenhouse equipment more quickly than the proposed non-linear control system. It was also shown that the regulation of the temperature and humidity was performed very smoothly by changing the reference signals according to the weather conditions outside the greenhouse. The ability of these two control systems was graphically demonstrated for temperature and humidity responses as well as for the signals sent to the actuators.

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17

Kawano, Masashi. "Optimal Control for Window Systems and Lighting Control Systems." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 87, no. 9 (2003): 738–41. http://dx.doi.org/10.2150/jieij1980.87.9_738.

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18

Hayashi, Kentaro. "Cyber-Security Controls for Industrial Control Systems." JAPAN TAPPI JOURNAL 68, no. 3 (2014): 275–80. http://dx.doi.org/10.2524/jtappij.68.275.

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19

Srinivas, P. Siva, M. Rambabu, and Dr G. V. Nagesh Kumar. "PQ Control Based Grid Connected DG Systems." International Journal of Engineering Research 4, no. 10 (October 1, 2015): 523–26. http://dx.doi.org/10.17950/ijer/v4s10/1002.

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20

Zulfizar, Muratova. "INTELLIGENT CONTROL METHODS FOR STREET LIGHTING SYSTEMS." American Journal of Applied Science and Technology 3, no. 12 (December 1, 2023): 24–30. http://dx.doi.org/10.37547/ajast/volume03issue12-06.

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This comprehensive article explores the transformative journey of street lighting systems, highlighting recent advancements in intelligent control models, methods, and algorithms. The narrative encompasses the evolution from traditional, fixed-schedule lighting to dynamic, adaptive systems that respond to real-time data, sensors, and communication technologies. The article delves into the benefits, challenges, and future outlook of these innovations, emphasizing the role of machine learning, IoT integration, and specialized algorithms. It also discusses the positive impacts on energy efficiency, safety, and the overall development of smart cities.

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21

Mistry, Vrushank. "Wireless Communication Technologies in HVAC Control Systems." International Journal of Science and Research (IJSR) 7, no. 7 (July 5, 2018): 1537–43. http://dx.doi.org/10.21275/sr24203192628.

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22

Diveev, A. I., and E. A. Sofronova. "Symmetric Control Systems." Herald of the Bauman Moscow State Technical University. Series Instrument Engineering, no. 2 (135) (June 2021): 37–51. http://dx.doi.org/10.18698/0236-3933-2021-2-37-51.

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The paper focuses on the properties of symmetric control systems, whose distinctive feature is that the solution of the optimal control problem for an object, the mathematical model of which belongs to the class of symmetric control systems, leads to the solution of two problems. The first optimal control problem is the initial one; the result of its solution is a function that ensures the optimal movement of the object from the initial state to the terminal one. In the second problem, the terminal state is the initial state, and the initial state is the terminal state. The complexity of the problem being solved is due to the increase in dimension when the models of all objects of the group are included in the mathematical model of the object, as well as the emerging dynamic phase constraints. The presence of phase constraints in some cases leads to the target functional having several local extrema. A theorem is proved that under certain conditions the functional is not unimodal when controlling a group of objects belonging to the class of symmetric systems. A numerical example of solving the optimal control problem with phase constraints by the Adam gradient method and the evolutionary particle swarm method is given. In the example, a group of two symmetrical objects is used as a control object

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23

Kalyaev, I. A., and E. V. Melnik. "Trusted Control Systems." Mekhatronika, Avtomatizatsiya, Upravlenie 22, no. 5 (May 17, 2021): 227–36. http://dx.doi.org/10.17587/mau.22.227-236.

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Nowadays, the problem of ensuring security of systems with a critical mission has become particularly relevant. An increased opportunity for unauthorized exposure on such systems via hardware, software and communication networks is the main reason to discuss this problem. It is confirmed by a plenty of accidents when equipment is out of order by means of malicious embedded elements and viruses. Currently, in the Russian Federation the majority of control systems are based on foreign hardware and software platforms, including strategic enterprises and objects with a critical mission. Herewith, the proportion of foreign microelectronic components in such systems is more than 85 %. The article is devoted to the development of scientific basis and techniques of the assurance assessment to control systems of objects with a critical mission. It was shown, that assurance assessment to a control system is a broader index than its reliability and fault tolerance. Such index must integrate various evidences and approvals, which can be objective, based on physical and mathematical assurance assessment methods, as well as they can be subjective, based on the experts experience. A method of assurance assessment to a control system of objects with a critical mission, based on Shortliffe’s scheme, was proposed in this paper. The Shortliffe’s scheme is used in the theories of fuzzy logic for assurance assessment to a hypothesis on the basis of various evidences and statements. An important advantage of a Shortliffe’s scheme is the set of evidences, which can be broadened and augmented (for instance, on the basis of obtained experience). It allows us to clarify a certainty factor. The assessment methods of truth degree of terminal statements of various types, including those, which require the combination of objective and subjective methods of their truth degree assessment, are proposed. The proposed assurance assessment method for national development and creation standards of control systems of objects with a critical mission allows to significantly increase their functional security.

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24

Joslyn, Cliff. "Semantic control systems." World Futures 45, no. 1-4 (December 1995): 87–123. http://dx.doi.org/10.1080/02604027.1995.9972555.

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25

Abbott, K. M. "Modernising Control Systems." Electronics and Power 31, no. 7 (1985): 528. http://dx.doi.org/10.1049/ep.1985.0323.

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26

Taylor, P. M. "Digital Control Systems." Electronics and Power 32, no. 1 (1986): 77. http://dx.doi.org/10.1049/ep.1986.0043.

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27

Feng, Yuming, Chuandong Li, Tingwen Huang, and Weijing Zhao. "Alternate control systems." Advances in Difference Equations 2014, no. 1 (2014): 305. http://dx.doi.org/10.1186/1687-1847-2014-305.

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28

Young, K. W., R. Muehlhaeusser, R. S. H. Piggin, and P. Rachitrangsan. "Agile control systems." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 215, no. 2 (February 1, 2001): 189–95. http://dx.doi.org/10.1243/0954407011525575.

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To cope with unpredictable demand and a wide variety of products, future production systems require agility. To realize manufacturing agility, the control system has to respond and adapt to variations in real-world, dynamic production environments. The control system has to promote requirements such as reduced complexity, increased flexibility, adaptation in real time, extensibility, heterogeneity and autonomous operation. A control system architecture is proposed ensuring manufacturing agility by adapting quickly and cheaply to changes in the production environment.

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29

Angeli, D., and E. D. Sontag. "Monotone control systems." IEEE Transactions on Automatic Control 48, no. 10 (October 2003): 1684–98. http://dx.doi.org/10.1109/tac.2003.817920.

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30

Spinellis, D. "Version control systems." IEEE Software 22, no. 5 (September 2005): 108–9. http://dx.doi.org/10.1109/ms.2005.140.

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31

Naidu,, DS, and I. Kolmanovsky,. "Optimal Control Systems." Applied Mechanics Reviews 57, no. 1 (January 1, 2004): B3—B4. http://dx.doi.org/10.1115/1.1641776.

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32

Lamego, M. M. "Automata control systems." IET Control Theory & Applications 1, no. 1 (January 1, 2007): 358–71. http://dx.doi.org/10.1049/iet-cta:20060009.

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33

Polyak, B. T., Mario Sznaier, P. S. Shcherbakov, and M. Halpern. "SUPERSTABLE CONTROL SYSTEMS." IFAC Proceedings Volumes 35, no. 1 (2002): 199–204. http://dx.doi.org/10.3182/20020721-6-es-1901.00114.

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34

Pauly, Thomas. "Distributed control systems." Electronics and Power 33, no. 9 (1987): 573. http://dx.doi.org/10.1049/ep.1987.0351.

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35

Thompson, David L. "Motor Control Systems." Electronic Systems News 1988, no. 3 (1988): 28. http://dx.doi.org/10.1049/esn.1988.0060.

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36

Dzurňáková, Zuzana, and Ján Sarnovský. "Hybrid Control Systems." IFAC Proceedings Volumes 33, no. 13 (June 2000): 119–23. http://dx.doi.org/10.1016/s1474-6670(17)37176-8.

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37

Su, Sophia, Kevin Baird, and Herb Schoch. "Management control systems." Journal of Accounting & Organizational Change 13, no. 1 (March 6, 2017): 2–24. http://dx.doi.org/10.1108/jaoc-03-2015-0032.

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Purpose This paper aims to examine the association between the interactive and diagnostic approaches to using controls and Miller and Friesen’s (1984) organizational life cycle (OLC) stages (birth, growth, maturity, revival). Design/methodology/approach Data were collected from a random sample of 343 general managers in Australian manufacturing organizations. Findings The results indicate that both approaches are used to a greater extent in the growth and revival stages than the birth and maturity stages, whereas the interactive and diagnostic approaches are used to a similar extent in each of the four OLC stages. Research limitations/implications This study contributes to the management control system literature by examining the use of the interactive and diagnostic approaches from an OLC perspective. The findings highlight that the complementary nature of the interactive and diagnostic approaches applies in each OLC stages, and suggest that similar attention should be placed on the use of both the interactive and diagnostic approaches in each OLC stage. Practical implications The study provides managers with an insight into the prevalence of the use of interactive and diagnostic approaches across the birth, growth, maturity and revival stages. Originality/value This study contributes to the management control system literature by adopting the configuration approach to examine how multiple contingent variables simultaneously affect the approach to using controls. Specifically, this study examines how organizations adjust their emphasis on Simons’ (1995) interactive and diagnostic approaches to using controls as they move across different development stages. These development stages were labelled as OLC stages and are determined based on the simultaneous consideration of multiple contingent factors.

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38

Shea, Gerald. "Quality Control Systems." Technometrics 32, no. 2 (May 1990): 221–22. http://dx.doi.org/10.1080/00401706.1990.10484641.

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39

Vassilyev, S. N., A. Yu Kelina, Y. I. Kudinov, and F. F. Pashchenko. "Intelligent Control Systems." Procedia Computer Science 103 (2017): 623–28. http://dx.doi.org/10.1016/j.procs.2017.01.088.

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40

Karimi, Hamid Reza, Huijun Gao, James Lam, and Haiping Du. "Vibration control systems." Mechatronics 24, no. 4 (June 2014): 287–88. http://dx.doi.org/10.1016/j.mechatronics.2014.05.001.

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41

Edward Lyshevski, Sergey. "Automotive control systems." Automatica 38, no. 6 (June 2002): 1089. http://dx.doi.org/10.1016/s0005-1098(01)00291-6.

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42

Lisanti, Thomas F. "Dynamitron control systems." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 241, no. 1-4 (December 2005): 839–43. http://dx.doi.org/10.1016/j.nimb.2005.07.140.

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43

Kaczorek, Tadeusz. "Nonlinear control systems." Control Engineering Practice 5, no. 5 (May 1997): 733–34. http://dx.doi.org/10.1016/s0967-0661(97)85452-4.

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44

Kheir, Naim A. "Feedback control systems." Automatica 22, no. 6 (November 1986): 765. http://dx.doi.org/10.1016/0005-1098(86)90021-x.

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45

Wittenmark, Björn. "Control systems engineering." Automatica 23, no. 3 (May 1987): 417. http://dx.doi.org/10.1016/0005-1098(87)90020-3.

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46

Banks, Stepehen. "Control systems engineering." Automatica 24, no. 1 (January 1988): 110. http://dx.doi.org/10.1016/0005-1098(88)90016-7.

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47

Phillips, Charles L., and Royce D. Harbor. "Feedback control systems." Automatica 26, no. 4 (July 1990): 824–25. http://dx.doi.org/10.1016/0005-1098(90)90061-l.

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48

Zagalak, Petr. "Singular control systems." Automatica 28, no. 3 (May 1992): 649–50. http://dx.doi.org/10.1016/0005-1098(92)90193-j.

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49

Trentelman, Harry L. "Feedback control systems." Automatica 32, no. 6 (June 1996): 945–46. http://dx.doi.org/10.1016/0005-1098(96)89428-3.

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50

Kaiser, Mark J. "Control systems engineering." Control Engineering Practice 3, no. 4 (April 1995): 593. http://dx.doi.org/10.1016/0967-0661(95)90093-4.

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