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Journal articles on the topic 'Control systems modelling'

Author: Grafiati

Published: 4 June 2021

Last updated: 12 February 2022

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1

Cao, S. G., and G. Feng. "Modelling of Complex Control Systems." IFAC Proceedings Volumes 28, no. 14 (June 1995): 849–54. http://dx.doi.org/10.1016/s1474-6670(17)46935-7.

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2

José Alves da Silva, Neto, Giacaglia Giorgio Eugenio Oscare, Lamas Wendell de Queiroz, and Bargos Fabiano Fernandes. "MODELLING AND CONTROL OF OPEN SYSTEMS." Engineering Research: technical reports 7, no. 1 (2016): 1–19. http://dx.doi.org/10.32426/engresv7n1-001.

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3

Niemi, A. J., and R. Ylinen. "Modelling of Grinding Systems for Control." IFAC Proceedings Volumes 20, no. 8 (August 1987): 187–92. http://dx.doi.org/10.1016/s1474-6670(17)59090-4.

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4

Cobelli, Claudio, Luigi Mariani, and Sheldon M. Stern. "Modelling and Control in Biomedical Systems." Journal of Clinical Engineering 14, no. 4 (July 1989): 349. http://dx.doi.org/10.1097/00004669-198907000-00014.

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5

Butler, David, Raziyeh Farmani, and Slobodan Djordjević. "Water systems modelling, data and control." Urban Water Journal 17, no. 8 (September 13, 2020): 681. http://dx.doi.org/10.1080/1573062x.2020.1840086.

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6

Siettos, Constantinos I., Yannick De Decker, and Georgios C. Georgiou. "Complex Systems Modelling, Analysis, and Control." Journal of Applied Mathematics 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/508176.

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7

Safonov, M. G. "Large-scale systems modelling and control." Proceedings of the IEEE 73, no. 8 (1985): 1340–41. http://dx.doi.org/10.1109/proc.1985.13289.

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8

Lirov, Y., E. Y. Rodin, B. G. McElhaney, and L. W. Wilbur. "Artificial intelligence modelling of control systems." SIMULATION 50, no. 1 (January 1988): 12–24. http://dx.doi.org/10.1177/003754978805000103.

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9

Dong, Wenbin, Rebing Wu, Xiaohu Yuan, Chunwen Li, and Tzyh-Jong Tarn. "The modelling of quantum control systems." Science Bulletin 60, no. 17 (September 2015): 1493–508. http://dx.doi.org/10.1007/s11434-015-0863-3.

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10

Crabtree, B., and E. A. Newsholme. "Modelling and quantifying metabolic control systems." Trends in Biochemical Sciences 10, no. 5 (May 1985): 188–89. http://dx.doi.org/10.1016/0968-0004(85)90185-9.

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11

Vatau, Doru, Sorin Musuroi, Constantin Barbulescu, and Marius Babescu. "PV systems modelling and optimal control." Energy Conversion and Management 84 (August 2014): 448–56. http://dx.doi.org/10.1016/j.enconman.2014.04.032.

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12

Rees, S. E., E. R. Carson, D. D. Feng, and S. Andreassen. "Modelling and control in biomedical systems." Computer Methods and Programs in Biomedicine 104, no. 2 (November 2011): 27–28. http://dx.doi.org/10.1016/j.cmpb.2011.09.004.

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13

Weiping, J., and T. Hanqin. "FUZZY MODELLING ON CROP GROWTH CONTROL SYSTEMS." Acta Horticulturae, no. 230 (September 1988): 241–44. http://dx.doi.org/10.17660/actahortic.1988.230.30.

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14

Olsson, G., and B. Newell. "Wastewater Treatment Systems: Modelling, Diagnosis and Control." Water Intelligence Online 4 (December 30, 2015): 9781780402864. http://dx.doi.org/10.2166/9781780402864.

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15

Perić, Nedjeljko, Ivan Petrović, and Ante Magzan. "Modelling and Control of Water Supply Systems." IFAC Proceedings Volumes 31, no. 20 (July 1998): 543–48. http://dx.doi.org/10.1016/s1474-6670(17)41852-0.

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16

Betous-Almeida, Cláudia, and Karama Kanoun. "Dependability modelling of instrumentation and control systems." Safety Science 42, no. 5 (June 2004): 457–80. http://dx.doi.org/10.1016/j.ssci.2003.09.011.

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17

Lewin, Daniel R., Miriam Faigon, Avraham Fuchs, and Raphael Semiat. "Modelling and control of two-phase systems." Computers & Chemical Engineering 16 (May 1992): S149—S156. http://dx.doi.org/10.1016/s0098-1354(09)80017-3.

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18

Tadé, Moses O., Budi H. Bisowarno, and Yu-Chu Tian. "Modelling and Control of Reactive Distillation Systems." IFAC Proceedings Volumes 37, no. 1 (January 2004): 31–38. http://dx.doi.org/10.1016/s1474-6670(17)38706-2.

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19

Gomide, F. A. C., W. C. Amaral, J. E. R. Cury, L. Gimeno, M. L. Andrade, and M. J. Mendes. "Modelling, Optimization and Control of Subway Systems." IFAC Proceedings Volumes 20, no. 3 (July 1987): 165–68. http://dx.doi.org/10.1016/s1474-6670(17)55892-9.

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20

Vasileiadou, S., D. Kalligeropoulos, and N. Karcanias. "Systems, Modelling and Control in Ancient Greece." Measurement and Control 36, no. 3 (April 2003): 76–80. http://dx.doi.org/10.1177/002029400303600302.

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21

Vasileiadou, S., D. Kalligeropoulos, and N. Karcanias. "Systems, Modelling and Control in Ancient Greece." Measurement and Control 36, no. 3 (April 2003): 81–86. http://dx.doi.org/10.1177/002029400303600303.

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22

Vasileiadou, S., D. Kalligeropoulos, and N. Karcanias. "Systems, Modelling and Control in Ancient Greece." Measurement and Control 36, no. 3 (April 2003): 87–90. http://dx.doi.org/10.1177/002029400303600304.

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23

Rosa, Marcelo, Marco A. C. Barbosa, and Marcelo Teixeira. "Service-based manufacturing systems: modelling and control." International Journal of Production Research 57, no. 11 (November 16, 2018): 3421–34. http://dx.doi.org/10.1080/00207543.2018.1535723.

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24

Estévez, E., and M. Marcos. "FORMAL MODELLING OF INDUSTRIAL DISTRIBUTED CONTROL SYSTEMS." IFAC Proceedings Volumes 38, no. 1 (2005): 135–40. http://dx.doi.org/10.3182/20050703-6-cz-1902.01064.

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25

Dua, Pinky, and Efstratios N. Pistikopoulos. "Modelling and control of drug delivery systems." Computers & Chemical Engineering 29, no. 11-12 (October 2005): 2290–96. http://dx.doi.org/10.1016/j.compchemeng.2005.05.014.

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26

Waller, Michael H. "Book Review: Control Systems Modelling and Analysis." International Journal of Electrical Engineering & Education 23, no. 4 (October 1986): 378–79. http://dx.doi.org/10.1177/002072098602300434.

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27

Mladenova, Clementina. "Mathematical modelling and control of manipulator systems." Robotics and Computer-Integrated Manufacturing 8, no. 4 (1991): 233–42. http://dx.doi.org/10.1016/0736-5845(91)90035-q.

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28

Boukabou, A., and N. Mansouri. "Neural Predictive Control of Unknown Chaotic Systems." Nonlinear Analysis: Modelling and Control 10, no. 2 (April 25, 2005): 95–106. http://dx.doi.org/10.15388/na.2005.10.2.15125.

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In this work, a neural networks is developed for modelling and controlling a chaotic system based on measured input-output data pairs. In the chaos modelling phase, a neural network is trained on the unknown system. Then, a predictive control mechanism has been implemented with the neural networks to reach the close neighborhood of the chosen unstable fixed point embedded in the chaotic systems. Effectiveness of the proposed method for both modelling and prediction-based control on the chaotic logistic equation and Hénon map has been demonstrated.

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29

Mota, Alexandre Manuel, Pedro Fonseca, and José A. Fonseca. "Systems Modelling and Identification in CAN based Distributed Control Systems." IFAC Proceedings Volumes 33, no. 30 (November 2000): 83–89. http://dx.doi.org/10.1016/s1474-6670(17)36734-4.

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30

Buzacott, J. A. "Modelling manufacturing systems." Robotics and Computer-Integrated Manufacturing 2, no. 1 (January 1985): 25–32. http://dx.doi.org/10.1016/0736-5845(85)90004-3.

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31

PAWLAK, Mariusz, and Cezary BOCIANIAK. "Modelling of Control Systems for Superheated Steam Temperature." AUTOMATYKA, ELEKTRYKA, ZAKLOCENIA 11, no. 1(39)2020 (March 31, 2020): 22–30. http://dx.doi.org/10.17274/aez.2020.39.02.

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32

Amoskov, Victor, Daria Arslanova, Gennady Baranov, Alexandr Bazarov, Valery Belyakov, Alexey Firsov, Marina Kaparkova, et al. "Modelling EMS Maglev systems to develop control algorithms." Cybernetics and Physics, Volume 7, 2018, Number 1 (June 18, 2018): 11–17. http://dx.doi.org/10.35470/2226-4116-2018-7-1-11-17.

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Electromagnetic suspension (EMS) system for magnetically levitated vehicles can utilize different types of magnets, such as room temperature electromagnets, superconducting magnets as well as permanent magnets. In the course of the study the trichotomy has been applied to the electromagnetic suspension system. The EMS configuration considered in this paper has been treated as a combination of these three types of magnets modelled individually. Results of computations were compared to measurements on a working prototype that provided stable levitation of a platform weighing above 190 kg. A good agreement between the simulated and measured parameters enabled verification of the computational models for separate magnets, selection of efficient control algorithms for a combined EMS system, validation of numerical procedures for payload scaling for practical maglev applications. The combined EMS under study has demonstrated improved power consumption as compared to the conventional EMS. Optimal control algorithms for a combined EMS should factor in various criteria, including rapidity, stability, power consumption, weight, reliability, etc. Different types of magnets can be integrated into a single module to reach the desired performance. Hence, the optimum solution for the EMS design and relevant control algorithms should be searched within a common procedure using detailed computational models.

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33

YILDIRIM, Şahin, Nihat ÇABUK, and Veli BAKIRCIOĞLU. "Modelling and Control of Proposed Two Dodecacopter Systems." International Journal of Applied Mathematics Electronics and Computers 8, no. 2 (June 30, 2020): 34–38. http://dx.doi.org/10.18100/ijamec.698462.

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34

Young, P., M. Lees, A. Chotai, and W. Tych. "The Modelling and Multivariable Control of Glasshouse Systems." IFAC Proceedings Volumes 26, no. 2 (July 1993): 1139–42. http://dx.doi.org/10.1016/s1474-6670(17)48648-4.

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35

Di Benedetto, Maria Domenica, and Giordano Pola. "Networked Embedded Control Systems: from Modelling to Implementation." Electronic Proceedings in Theoretical Computer Science 124 (August 22, 2013): 9–13. http://dx.doi.org/10.4204/eptcs.124.3.

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36

Demenkova, T. A., O. A. Korzhova, and A. A. Phinenko. "Modelling of Algorithms for Solar Panels Control Systems." Procedia Computer Science 103 (2017): 589–96. http://dx.doi.org/10.1016/j.procs.2017.01.072.

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37

Jaworska, I., and A. Kurylowicz. "Modelling Uncertainty Estimation to Maintain Control Systems Stability." IFAC Proceedings Volumes 24, no. 3 (July 1991): 829–33. http://dx.doi.org/10.1016/s1474-6670(17)52452-0.

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38

Barbier, M., and J. Delmas. "Modelling, Control and Simulation of Flexible Assembly Systems." IFAC Proceedings Volumes 23, no. 3 (September 1990): 191–96. http://dx.doi.org/10.1016/s1474-6670(17)52556-2.

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39

Muhammad, A., A. Bargiela, and G. King. "Fuzzy and evolutionary modelling of nonlinear control systems." Mathematical and Computer Modelling 33, no. 4-5 (February 2001): 533–51. http://dx.doi.org/10.1016/s0895-7177(00)00259-4.

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40

Mei, F., Z. Man, and T. Nguyen. "Fuzzy modelling and tracking control of nonlinear systems." Mathematical and Computer Modelling 33, no. 6-7 (March 2001): 759–70. http://dx.doi.org/10.1016/s0895-7177(00)00278-8.

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41

Ntousakis, Ioannis A., Ioannis K. Nikolos, and Markos Papageorgiou. "On Microscopic Modelling of Adaptive Cruise Control Systems." Transportation Research Procedia 6 (2015): 111–27. http://dx.doi.org/10.1016/j.trpro.2015.03.010.

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42

Groumpos, Peter P., and Chrysostomos D. Stylios. "Modelling supervisory control systems using fuzzy cognitive maps." Chaos, Solitons & Fractals 11, no. 1-3 (January 2000): 329–36. http://dx.doi.org/10.1016/s0960-0779(98)00303-8.

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43

Yahiaoui, A. "Modelling and hybrid control of building lighting systems." Lighting Research & Technology 50, no. 8 (July 23, 2017): 1225–48. http://dx.doi.org/10.1177/1477153517712781.

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The development of an accurate control system for shading devices, essentially motorised Venetian blinds, relies on the rational use of daylight in buildings. This study proposes a control strategy for building lighting components, both daylighting and artificial lighting, to automatically adjust the light level in an office building as required and to block direct sunlight from entering the office, when necessary. The proposed control strategy is based on a hybrid statecharts model mainly consisting of a supervisory control system that chooses appropriate control actions for a current sky condition depending on the sky ratio and clearness index. These control actions are implemented to automatically adjust the blind height and slat angle, and turn on the artificial lighting only when needed. Real experiments in a test-cell demonstrate the effectiveness and flexibility of the proposed control strategy.

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44

Benerecetti, M., R. De Guglielmo, U. Gentile, S. Marrone, N. Mazzocca, R. Nardone, A. Peron, L. Velardi, and V. Vittorini. "Dynamic state machines for modelling railway control systems." Science of Computer Programming 133 (January 2017): 116–53. http://dx.doi.org/10.1016/j.scico.2016.09.002.

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45

Huang, Y. S., and P. J. Su. "Modelling and analysis of traffic light control systems." IET Control Theory & Applications 3, no. 3 (March 1, 2009): 340–50. http://dx.doi.org/10.1049/iet-cta:20070368.

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46

Dzieliński, Andrzej, and Waldemar Graniszewski. "NONLINEAR CONTROL SYSTEMS MODELLING USING LOCAL LINEAR MAPPINGS." IFAC Proceedings Volumes 35, no. 1 (2002): 181–86. http://dx.doi.org/10.3182/20020721-6-es-1901.01016.

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47

Kugi, Andreas, and Kurt Schlacher. "Modelling of distributed-parameter systems for control purposes." Mathematical and Computer Modelling of Dynamical Systems 14, no. 3 (June 2008): 177–78. http://dx.doi.org/10.1080/13873950701844816.

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48

Schlacher, Kurt, and Markus Schöberl. "Modelling, analysis and control of distributed parameter systems." Mathematical and Computer Modelling of Dynamical Systems 17, no. 1 (January 24, 2011): 1–2. http://dx.doi.org/10.1080/13873954.2010.537507.

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49

Wen, K., and Z. Geng. "Modelling and analysis of distributed networked control systems." IET Control Theory & Applications 6, no. 9 (2012): 1304. http://dx.doi.org/10.1049/iet-cta.2010.0589.

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50

Fong, N. H. Ben, and Robert H. Sturges Jr. "Dynamic modelling of multistage feedback-control manufacturing systems." International Journal of Manufacturing Research 2, no. 3 (2007): 263. http://dx.doi.org/10.1504/ijmr.2007.014725.

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