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Journal articles on the topic 'Fuzzy Control'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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1

HIROTA, Kaoru. "Fuzzy control." Journal of the Robotics Society of Japan 9, no. 2 (1991): 232–37. http://dx.doi.org/10.7210/jrsj.9.232.

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2

Dery, D. "Fuzzy Control." Journal of Public Administration Research and Theory 12, no. 2 (April 1, 2002): 191–216. http://dx.doi.org/10.1093/oxfordjournals.jpart.a003529.

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3

Liu, Derong, and Huaguang Zhang. "Fuzzy control." Automatica 39, no. 6 (June 2003): 1115–16. http://dx.doi.org/10.1016/s0005-1098(03)00064-5.

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4

Babuska, Robert, and Ebrahim Mamdani. "Fuzzy control." Scholarpedia 3, no. 2 (2008): 2103. http://dx.doi.org/10.4249/scholarpedia.2103.

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5

Qiu, Peihua. "Fuzzy Modeling and Fuzzy Control." Technometrics 50, no. 3 (August 2008): 408–9. http://dx.doi.org/10.1198/tech.2008.s901.

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6

FOULLOY, LAURENT, and SYLVIE GALICHET. "FUZZY SENSORS FOR FUZZY CONTROL." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 02, no. 01 (March 1994): 55–66. http://dx.doi.org/10.1142/s0218488594000067.

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This paper introduces sensors employing a fuzzy numeric to symbolic interface. The fundamental design considerations for this kind of fuzzy symbolic sensor, or fuzzy sensor, are formally presented. Then, the use of these components for fuzzy control is discussed and illustrated.
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7

Cios, Krzystof. "Fuzzy control and fuzzy systems." Neurocomputing 10, no. 1 (January 1996): 97–98. http://dx.doi.org/10.1016/s0925-2312(96)90014-4.

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8

Harris, C. J. "Fuzzy control & fuzzy systems." Automatica 28, no. 2 (March 1992): 443. http://dx.doi.org/10.1016/0005-1098(92)90135-3.

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9

HIROTA, Kaoru. "Fuzzy Reasoning and Fuzzy Control." Journal of the Society of Mechanical Engineers 93, no. 856 (1990): 202–8. http://dx.doi.org/10.1299/jsmemag.93.856_202.

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10

Foulloy, L., and S. Galichet. "Fuzzy control with fuzzy inputs." IEEE Transactions on Fuzzy Systems 11, no. 4 (August 2003): 437–49. http://dx.doi.org/10.1109/tfuzz.2003.814831.

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11

Foulloy, L., S. Galichet, and J. F. Josserand. "Fuzzy Components for Fuzzy Control." IFAC Proceedings Volumes 27, no. 3 (June 1994): 109–13. http://dx.doi.org/10.1016/s1474-6670(17)46093-9.

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12

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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13

Pavlica, Vladimir, and Dušan Petrovački. "About simple fuzzy control and fuzzy control based on fuzzy relational equations." Fuzzy Sets and Systems 101, no. 1 (January 1999): 41–47. http://dx.doi.org/10.1016/s0165-0114(97)00057-2.

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14

Yamada, Shinichi. "Touch at Fuzzy Control. Fuzzy Control Changes from Analytical Control to Human Control." IEEJ Transactions on Industry Applications 113, no. 1 (1993): 1–8. http://dx.doi.org/10.1541/ieejias.113.1.

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15

Li, Sheng Qian, and Xiao Jing Yang. "Design and Simulation for Control System of Tobacco Leaf Roasting Based on Fuzzy-PID Control." Advanced Materials Research 328-330 (September 2011): 2055–58. http://dx.doi.org/10.4028/www.scientific.net/amr.328-330.2055.

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This paper aimed at the characteristics of the control system of baking in different period of tobacco leaf roasting process, intelligent control system of tobacco leaf roast control tactics based on fuzzy-PID is proposed. A designing method and Implementation algorithm of fuzzy-PID is given. For the difficulty of traditional PID controller parameters adjusting, the PID parameters were adjusted online by using fuzzy reasoning method. It can combine the advantages of general fuzzy control and traditional PID control in this method, and it is simulated and compared with general PID control on the simulation platform of MATLAB control box. The simulation results show that the fuzzy self-tuning PID controller has good dynamic response curve, short response time, small overmodulation, high steady state precision, and good dynamic and static performance by comparing with the traditional PID controller. Therefore temperature and humidity control of tobacco leaf roasting will perfectly improved by fuzz PID control.
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16

Piskunov, Alexandre. "Fuzzy implication in fuzzy systems control." Fuzzy Sets and Systems 45, no. 1 (January 1992): 25–35. http://dx.doi.org/10.1016/0165-0114(92)90088-l.

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17

Gerla, Giangiacomo. "Fuzzy Logic Programming and Fuzzy Control." Studia Logica 79, no. 2 (March 2005): 231–54. http://dx.doi.org/10.1007/s11225-005-2977-0.

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18

ITO, OSAMU. "Crisp control and fuzzy control." Journal of the Robotics Society of Japan 6, no. 6 (1988): 563–72. http://dx.doi.org/10.7210/jrsj.6.6_563.

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19

Bugarin, A., S. Barro, and R. Ruiz. "Fuzzy Control Architectures." Journal of Intelligent and Fuzzy Systems 2, no. 2 (1994): 125–46. http://dx.doi.org/10.3233/ifs-1994-2203.

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20

Kim, Kwang-Choon, and Jong-Hwan Kim. "Multicriteria Fuzzy Control." Journal of Intelligent and Fuzzy Systems 2, no. 3 (1994): 279–88. http://dx.doi.org/10.3233/ifs-1994-2307.

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21

MATSUMOTO, Hisashi, and Shigeyuki MORITA. "Fuzzy Tranction Control." Transactions of the Japan Society of Mechanical Engineers Series C 58, no. 553 (1992): 2709–13. http://dx.doi.org/10.1299/kikaic.58.2709.

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22

Raber, Rudi. "Fuzzy in control." Sensor Review 14, no. 3 (September 1994): 26–28. http://dx.doi.org/10.1108/eum0000000004236.

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23

Ha, T. Y., Z. Binder, P. Horácek, and R. Perret. "Fuzzy Supervisory Control." IFAC Proceedings Volumes 31, no. 25 (September 1998): 121–24. http://dx.doi.org/10.1016/s1474-6670(17)36371-1.

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24

Filev, Dimiter, and Plamen Angelov. "Fuzzy optimal control." Fuzzy Sets and Systems 47, no. 2 (April 1992): 151–56. http://dx.doi.org/10.1016/0165-0114(92)90172-z.

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25

Efstathiou, Janet. "Modern fuzzy control." Fuzzy Sets and Systems 70, no. 2-3 (March 1995): 131–33. http://dx.doi.org/10.1016/0165-0114(94)00253-4.

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26

Szmidt, Eulalia. "Fuzzy control systems." Control Engineering Practice 4, no. 9 (September 1996): 1331. http://dx.doi.org/10.1016/0967-0661(96)81490-0.

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27

Johnston, R. "Fuzzy logic control." Microelectronics Journal 26, no. 5 (July 1995): 481–95. http://dx.doi.org/10.1016/0026-2692(95)98950-v.

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28

BATUR, C., and V. KASPARIAN. "Fuzzy adaptive control." International Journal of Systems Science 24, no. 2 (February 1993): 301–14. http://dx.doi.org/10.1080/00207729308949490.

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29

RAGOT, JOSÉ, and MICHEL LAMOTTE. "Fuzzy logic control." International Journal of Systems Science 24, no. 10 (October 1993): 1825–48. http://dx.doi.org/10.1080/00207729308949598.

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30

RAJU, G. V. S., JUN ZHOU, and ROGER A. KISNER. "Hierarchical fuzzy control." International Journal of Control 54, no. 5 (November 1991): 1201–16. http://dx.doi.org/10.1080/00207179108934205.

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31

Zhao, Yu Chi, and Jing Liu. "The Application of Fuzzy Control in Computer Control." Advanced Materials Research 756-759 (September 2013): 349–53. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.349.

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Fuzzy control theory is a computer numerical control theory based on fuzzy set theory, fuzzy language variable and fuzzy logic reasoning. It is widely used for it doesnt require exact mathematical model of controlled object in system design, so that fuzzy control has an advantage in researching high nonlinear system like inverted pendulum. However, rule explosion problem is unavoidable when we use fuzzy control theory to solve some multivariable system control problems such as inverted pendulum. This paper presents the application of the optimal control theory to reduce the input variable dimensions and the rules of the fuzzy controller through designing a fusion function, solving rule explosion problem successfully. The paper also discusses the control effect influenced by quantification factors, promoting performance quality of the fuzzy controller by setting threshold value to make quantification factors automatic regulation.
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32

Cheng, Chi-Bin. "Fuzzy process control: construction of control charts with fuzzy numbers." Fuzzy Sets and Systems 154, no. 2 (September 2005): 287–303. http://dx.doi.org/10.1016/j.fss.2005.03.002.

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33

Lu, Yongkun. "Adaptive-Fuzzy Control Compensation Design for Direct Adaptive Fuzzy Control." IEEE Transactions on Fuzzy Systems 26, no. 6 (December 2018): 3222–31. http://dx.doi.org/10.1109/tfuzz.2018.2815552.

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34

Huang, Feng Chen, Hui Feng, Zhen Li Ma, Xin Hui Yin, and Xue Wen Wu. "Application of Fuzzy PID Control in Sluice Control." Applied Mechanics and Materials 241-244 (December 2012): 1248–54. http://dx.doi.org/10.4028/www.scientific.net/amm.241-244.1248.

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Fuzzy control, based on traditional Proportional-Integral-Derivative (PID) control, is used to improve the management of a hydro-junction’s sluice scheduling. In this study, we combined the PID and Fuzzy control theories and determined the PID parameters of the fuzzy self-tuning method of a hydro-junction’s sluice. A fuzzy self-tuning PID controller and its algorithm were designed. In hydro-junction sluice control, the Fuzzy PID controller can modify PID parameters in real-time, resulting in a more dynamic response. The application of the fuzzy self-tuning PID controller in the CiHuai River project information integration system yielded very good results.
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35

He, Fang, Jia Han, and Qiang Wang. "Fuzzy Control with Adjustable Factors in Tension Control System." Advanced Materials Research 902 (February 2014): 201–6. http://dx.doi.org/10.4028/www.scientific.net/amr.902.201.

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The variable tension control system of strip winding is a nonlinear, strong coupling and time-varying system. Traditional fuzzy controller with fixed control rules cannot obtain the desired control performance of the strip winding system. So the fuzzy control algorithm with adjustable factorαis proposed, and the introduction of adjustable factors can change the fuzzy control rules. The tension fuzzy controller with adjustable factorαis designed, and simulation model of the system is established using Matlab software. The result of simulation shows that tension fluctuation of the tension fuzzy control system with adjustable factor get small, comparing the tension fuzzy control system with adjustable factors with the ordinary tension fuzzy control system. The tension fuzzy control system with adjustable factors has fast system response and strong anti-interference ability.
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36

Zeng, Wen Yi, and Qian Yin. "Control Algorithm of Interval-Valued Fuzzy Control." Advanced Materials Research 562-564 (August 2012): 2111–15. http://dx.doi.org/10.4028/www.scientific.net/amr.562-564.2111.

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In this paper, we use the similarity measure of interval-valued fuzzy sets to investigate approximate reasoning of interval-valued fuzzy sets, propose the mathematical model of interval-valued fuzzy control, and investigate its control algorithm.
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37

Triwiyatno, Aris, Mohammad Nuh, Ari Santoso, and I. Nyoman Sutantra. "Engine Torque Control of Spark Ignition Engine Using Robust Fuzzy Logic Control." International Journal of Engineering and Technology 3, no. 4 (2011): 352–58. http://dx.doi.org/10.7763/ijet.2011.v3.252.

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38

He, Haibo. "Fuzzy Modeling and Fuzzy Control [Book Review]." IEEE Computational Intelligence Magazine 3, no. 3 (August 2008): 8–10. http://dx.doi.org/10.1109/mci.2008.926613.

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39

Yuanguo Zhu. "Fuzzy Optimal Control for Multistage Fuzzy Systems." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 41, no. 4 (August 2011): 964–75. http://dx.doi.org/10.1109/tsmcb.2010.2102015.

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40

Gerla, Giangiacomo. "Fuzzy control as a fuzzy deduction system." Fuzzy Sets and Systems 121, no. 3 (August 2001): 409–25. http://dx.doi.org/10.1016/s0165-0114(00)00124-x.

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41

Sudibyo, Pandu, Yanu Shalahuddin, and Mochtar Yahya. "Single Axis Tracking PV Panel Using Fuzzy Logic Control." JTECS : Jurnal Sistem Telekomunikasi Elektronika Sistem Kontrol Power Sistem dan Komputer 1, no. 1 (January 1, 2021): 1. http://dx.doi.org/10.32503/jtecs.v1i1.646.

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Abstrak – Panel PV(Photovoltaic) merupakan teknologi yang mengubah energi cahaya matahari menjadi energi listrik. Maka dari itu untuk mendapatkan iradiansi maksinal perlu sistem solar tracker sebagai cara untuk optimalisasi penyerapan cahaya matahari. Pada penelitian ini membahas pembuatan model simulink solar tracker menggunakan kontroler fuzzy logic. Arah sinar matahari disensor mengguanakan 2 buah sensor LDR (Light Dependent Resistor) yang selanjutnya menjadi input logika fuzy. Sistem terdiri atas 4 komponen utama yaitu PV Modul ,Mikrokontroler, motor servo, sensor LDR(Light Dependent Resistor) yang selanjutnya menjadi input logika fuzy. Output logika fuzy berupa nilai yang kemudian diumpan ke servo untuk gerakan panel secara Single Axis. Aplikasi Matlab Simulink sebagai compiler dan pembuat permodelan sistem yang nantinya akan diupload ke mikrokontroler. Arah putaran motor servo ditentukan dengan menggunakan kendali logika fuzzy. Hasil pengujian membuktikan rata-rata tegangan panel PV lebih tinggi daripada panel tanpa kendali, dengan nilai rata-rata sebesar 14,35V.
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42

Calvo, Oscar, and Julyan H. E. Cartwright. "Fuzzy Control of Chaos." International Journal of Bifurcation and Chaos 08, no. 08 (August 1998): 1743–47. http://dx.doi.org/10.1142/s0218127498001443.

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43

Karthikeyan, R., K. Manickavasagam, Shikha Tripathi, and K. V. V. Murthy. "Neuro-Fuzzy-Based Control for Parallel Cascade Control." Chemical Product and Process Modeling 8, no. 1 (June 8, 2013): 15–25. http://dx.doi.org/10.1515/cppm-2013-0002.

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Abstract This paper discusses the application of adaptive neuro-fuzzy inference system (ANFIS) control for a parallel cascade control system. Parallel cascade controllers have two controllers, primary and secondary controllers in cascade. In this paper the primary controller is designed based on neuro-fuzzy approach. The main idea of fuzzy controller is to imitate human reasoning process to control ill-defined and hard to model plants. But there is a lack of systematic methodology in designing fuzzy controllers. The neural network has powerful abilities for learning, optimization and adaptation. A combination of neural networks and fuzzy logic offers the possibility of solving tuning problems and design difficulties of fuzzy logic. Due to their complementary advantages, these two models are integrated together to form more robust learning systems, referred to as adaptive neuro-fuzzy inference system (ANFIS). The secondary controller is designed using the internal model control approach. The performance of the proposed ANFIS-based control is evaluated using different case studies and the simulated results reveal that the ANFIS control approach gives improved servo and regulatory control performances compared to the conventional proportional integral derivative controller.
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44

Aung, Thae Thae Ei, and Zar Chi Soe. "Liquid Flow Control by Using Fuzzy Logic Controller." International Journal of Trend in Scientific Research and Development Volume-2, Issue-5 (August 31, 2018): 2190–93. http://dx.doi.org/10.31142/ijtsrd18263.

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45

Madhava, Meghna, N. Meghana, Mulpuru Supriya, Div ya, and Siddalingesh S. Navalgund. "Automatic Train Control System Using Fuzzy Logic Controller." Bonfring International Journal of Research in Communication Engineering 6, Special Issue (November 30, 2016): 56–61. http://dx.doi.org/10.9756/bijrce.8201.

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46

Girovský, Peter. "FUZZY CONTROL OF SYNCHRONOUS MOTOR WITH PERMANENT MAGNET." Acta Electrotechnica et Informatica 16, no. 4 (December 2016): 17–20. http://dx.doi.org/10.15546/aeei-2016-0027.

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47

Phu, Nguyen Dinh, Nguyen Nhut Hung, Ali Ahmadian, and Norazak Senu. "A New Fuzzy PID Control System Based on Fuzzy PID Controller and Fuzzy Control Process." International Journal of Fuzzy Systems 22, no. 7 (August 11, 2020): 2163–87. http://dx.doi.org/10.1007/s40815-020-00904-y.

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48

Chen,, Guanrong, Trung Tat Pham,, and NM Boustany,. "Introduction to Fuzzy Sets, Fuzzy Logic, and Fuzzy Control Systems." Applied Mechanics Reviews 54, no. 6 (November 1, 2001): B102—B103. http://dx.doi.org/10.1115/1.1421114.

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49

KATO, Akio, and Takashi MATSUBA. "Aircraft Attitude Control by Fuzzy Control." Journal of the Japan Society for Aeronautical and Space Sciences 47, no. 550 (1999): 419–26. http://dx.doi.org/10.2322/jjsass.47.419.

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50

KATO, Akio, and Daisuke INUKAI. "Control Augmentation Using Fuzzy Logic Control." JOURNAL OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 49, no. 570 (2001): 222–30. http://dx.doi.org/10.2322/jjsass.49.222.

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