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Journal articles on the topic 'Fuzzy Control'

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Published: 4 June 2021
Last updated: 15 June 2025

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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 (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 (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 (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 (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 (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 (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 (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 (1994): 109–13. http://dx.doi.org/10.1016/s1474-6670(17)46093-9.

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12

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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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 (1999): 41–47. http://dx.doi.org/10.1016/s0165-0114(97)00057-2.

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14

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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15

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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16

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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17

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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18

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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19

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

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20

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

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21

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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22

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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23

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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24

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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25

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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26

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

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27

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

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28

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

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29

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

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30

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

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31

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

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32

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

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33

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

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34

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

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35

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

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36

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

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37

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 (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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38

Arifin, Bustanul, Bhakti Yudho Suprapto, Sri Arttini Dwi Prasetyowati, and Zainuddin Nawawi. "Steering Control in Electric Power Steering Autonomous Vehicle Using Type-2 Fuzzy Logic Control and PI Control." World Electric Vehicle Journal 13, no. 3 (2022): 53. http://dx.doi.org/10.3390/wevj13030053.

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The steering system in autonomous vehicles is an essential issue that must be addressed. Appropriate control will result in a smooth and risk-free steering system. Compared to other types of controls, type-2 fuzzy logic control has the advantage of dealing with uncertain inputs, which are common in autonomous vehicles. This paper proposes a novel method for the steering control of autonomous vehicles based on type-2 fuzzy logic control combined with PI control. The primary control, type-2 fuzzy logic control, has three inputs—distance, navigation, and speed. The fuzzy system’s output is the steering angle value. This was used as input for the secondary control, PI control. This control is in charge of adjusting the motor’s position as a manifestation of the steering angle. The study results applied to the EPS system of autonomous vehicles revealed that type-2 fuzzy logic control and PI control produced better and smoother control than type-1 fuzzy logic control and PI. The slightest disturbance in the type-1 fuzzy logic control showed a significant change in steering, while this did not occur in the type-2 fuzzy logic control. The results indicate that type-2 fuzzy logic control and PI control could be used for autonomous vehicles by maintaining the comfort and safety of the users.
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39

Wang, Wei Ping, and Li Zhou. "Research on Intelligent Control Technology with Building Energy Control Model Based on Intelligent Control Algorithm." Advanced Materials Research 1014 (July 2014): 329–32. http://dx.doi.org/10.4028/www.scientific.net/amr.1014.329.

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For the current smart building energy control algorithms are still large energy loss, poor energy-saving effect and other issues, this paper presents a fuzzy neural network algorithm based on improved BP algorithm, the improved algorithm of BP neural network algorithm first reverse dissemination and weighting coefficients are adjusted to accelerate the convergence rate of the original algorithm, and then build the improved BP neural network algorithm for fuzzy neural network, and then to improve it fuzzy membership function parameters to improve the efficiency of fuzzy neural network learning. Simulation results show that the proposed fuzzy neural network algorithm based on improved BP algorithm in the intelligent building energy control, with the algorithm is better than traditional BP neural network energy savings, reducing the energy loss rate.
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40

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 (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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41

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

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42

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

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43

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

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44

Yang, Jie, Yingkai Guo, and Xin Huang. "A software development system for fuzzy control." Robotica 18, no. 4 (2000): 375–80. http://dx.doi.org/10.1017/s0263574799002398.

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Fuzzy control has been widely applied in industrial controls and domestic electrical equipment. The automatic learning of fuzzy rules is a key technique in fuzzy control. In this paper, a software development system for fuzzy control is presented. Since the learning of fuzzy rules can be seen as finding the best classifications of fuzzy memberships of input-output variables in a fuzzy controller, it can also be seen as the combination optimization of input-output fuzzy memberships. Multi-layer feedforward network and genetic algorithms (GA) can be used for the automatic learning of fuzzy rules. The algorithms and their characteristics are described. The software development system has been successfully used for the design of some fuzzy controllers.
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45

Hayashi, Kenichiro, Akifumi Otsubo, and Kazuhiko Shiranita. "Realization of PID Control by Fuzzy Inference and its Application to Hybrid Control." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 6 (1999): 491–98. http://dx.doi.org/10.20965/jaciii.1999.p0491.

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First, we propose a more practical method for realizing nonlinear and linear PID control by fuzzy inference. By applying the simplified direct inference of fuzzy inference to only a few simple fuzzy control rules, both nonlinear PID control based on a conventional PID controller and linear PID control are more simply realized than by previous methods. Next, we propose a hybrid control method in which fuzzy and PID control are hybridized for mutual compensation of both types of control. In this proposal, fuzzy control (nonlinear PID control) and PID control (linear PID control), realized by the simplified direct inference of fuzzy inference, are combined in parallel and used alternately. The main characteristics of this method are high-speed control realized by applying the simplified direct inference of fuzzy inference and smoothness of switching control guaranteed by fuzzy switching of fuzzy and PID control.
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46

Chiu and Peng. "Design of Takagi-Sugeno Fuzzy Control Scheme for Real World System Control." Sustainability 11, no. 14 (2019): 3855. http://dx.doi.org/10.3390/su11143855.

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In this study, a novelty dual Takagi-Sugeno (TS) fuzzy control scheme (DTSFCS) is proposed for real world system control. We propose using a ball robot (BR) system control problem, where the BR has the ability to move omnidirectionally. The proposed control scheme combines two fuzzy control approaches for a BR. In this fuzzy control approach, the TS fuzzy model was adopted for the fuzzy modeling of the BR. The concept of parallel distributed compensation (PDC) was utilized to develop a fuzzy control scheme from the TS fuzzy models. The linear matrix inequalities (LMIs) can formulate sufficient conditions. Moreover, in this study, the motors of the BR were mounted on two orthogonal axes. Then, the dual TS fuzzy controller was designed to independently operate without coupling. Finally, the efficiency of the proposed control scheme is illustrated by the experimental and simulation results that are presented in this study.
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47

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

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48

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 (2020): 2163–87. http://dx.doi.org/10.1007/s40815-020-00904-y.

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49

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

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50

Zhou, Yang, Zhi Wang, and Li Yang. "Application of Fuzzy PID Control Based on GA in Control Valve." Applied Mechanics and Materials 668-669 (October 2014): 445–49. http://dx.doi.org/10.4028/www.scientific.net/amm.668-669.445.

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In order to improve the dynamic characteristic of regulating valve system based on traditional fuzzy PID, a control strategy was presented and this method combines fuzzy PID and improved GA (genetic algorithm) to optimize fuzzy rules. Finally a simulation was carried on in MATLAB. The results show that: Compared with the traditional PID, the fuzzy PID enables the system to achieve better dynamic characteristics.
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