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Journal articles on the topic 'Control Variable'

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Author: Grafiati

Published: 8 September 2023

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1

Chun, Changmook, Le Dihn-Phong, Byungchan Kim, and Sungchul Kang. "Manipulability-Based Variable Damping Control in Robotic Manipulation." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2010.5 (2010): 416–20. http://dx.doi.org/10.1299/jsmeicam.2010.5.416.

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2

Dursun, Emre Hasan, and Akif Durdu. "Speed Control of a DC Motor with Variable Load Using Sliding Mode Control." International Journal of Computer and Electrical Engineering 8, no. 3 (2016): 219–26. http://dx.doi.org/10.17706/ijcee.2016.8.3.219-226.

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3

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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4

Nashed, Maged N. F. "Variable Angle of SRG for Wind Energy Control Application." International Journal of Engineering Research 4, no. 2 (February 1, 2015): 55–59. http://dx.doi.org/10.17950/ijer/v4s2/203.

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5

Buchli, Jonas, Freek Stulp, Evangelos Theodorou, and Stefan Schaal. "Learning variable impedance control." International Journal of Robotics Research 30, no. 7 (April 2011): 820–33. http://dx.doi.org/10.1177/0278364911402527.

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6

Li, H. X., H. B. Gatland, and A. W. Green. "Fuzzy variable structure control." IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) 27, no. 2 (April 1997): 306–12. http://dx.doi.org/10.1109/3477.558824.

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7

Niu, Hong, Qingling Zhang, Chunyu Yang, and Fenglan Bai. "Variable structure control for three-variable autocatalytic reaction." Journal of Control Theory and Applications 11, no. 3 (July 4, 2013): 393–400. http://dx.doi.org/10.1007/s11768-013-2044-8.

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8

Tan, Han-Shue, and Yuen-Kwok Chin. "Vehicle Traction Control: Variable-Structure Control Approach." Journal of Dynamic Systems, Measurement, and Control 113, no. 2 (June 1, 1991): 223–30. http://dx.doi.org/10.1115/1.2896369.

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A longitudinal one-wheel vehicle model is described for both anti-lock braking and anti-span acceleration. Based on this vehicle model, sufficient conditions for applying sliding-mode control to vehicle traction are derived via Lyapunov Stability Theory. With the understanding of these sufficient conditions, control laws are designed to control vehicle traction. Both the sufficient conditions and the control laws are verified using computer simulations.

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9

Wei, Jianli, Shida Tian, and Xinghua Yao. "Variable Structure Control for Hypersonic Vehicle Based on Model Reference." International Journal of Applied Physics and Mathematics 5, no. 2 (2015): 144–52. http://dx.doi.org/10.17706/ijapm.2015.5.2.144-152.

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10

Riccardi, Fabio, Muhammad Farooq Haydar, Simone Formentin, and Marco Lovera. "Control of variable-pitch quadrotors." IFAC Proceedings Volumes 46, no. 19 (2013): 206–11. http://dx.doi.org/10.3182/20130902-5-de-2040.00143.

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11

Skogestad, Sigurd, and Manfred Morari. "Variable selection for decentralized control." Modeling, Identification and Control: A Norwegian Research Bulletin 13, no. 2 (1992): 113–25. http://dx.doi.org/10.4173/mic.1992.2.3.

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12

McIntosh, Russ. "Savings with Variable Speed Control." Energy Engineering 111, no. 3 (March 26, 2014): 7–31. http://dx.doi.org/10.1080/01998595.2014.10816364.

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13

Hung, J. Y., W. Gao, and J. C. Hung. "Variable structure control: a survey." IEEE Transactions on Industrial Electronics 40, no. 1 (1993): 2–22. http://dx.doi.org/10.1109/41.184817.

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14

Wu, Yuqiang, Xinghuo Yu, Lijun Zhang, Yu Kang, and Ningsu Luo. "Variable Structure Control and Applications." Mathematical Problems in Engineering 2013 (2013): 1–2. http://dx.doi.org/10.1155/2013/589759.

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15

Stefanopoulou, A. G., J. S. Freudenberg, and J. W. Grizzle. "Variable camshaft timing engine control." IEEE Transactions on Control Systems Technology 8, no. 1 (2000): 23–34. http://dx.doi.org/10.1109/87.817689.

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16

YU, XINGHUO. "Discrete variable structure control systems." International Journal of Systems Science 24, no. 2 (February 1993): 373–86. http://dx.doi.org/10.1080/00207729308949495.

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17

Cucuzzella, Michele, Gian Paolo Incremona, and Antonella Ferrara. "Event-triggered variable structure control." International Journal of Control 93, no. 2 (February 8, 2019): 252–60. http://dx.doi.org/10.1080/00207179.2019.1575977.

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18

Alvarez-Rodríguez, Sergio, Gerardo Flores, and Noé Alcalá Ochoa. "Variable Gains Sliding Mode Control." International Journal of Control, Automation and Systems 17, no. 3 (February 22, 2019): 555–64. http://dx.doi.org/10.1007/s12555-018-0095-9.

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19

García-Bustos, Sandra, Mónica Mite, and Francisco Vera. "Control Charts with Variable Dimension for Linear Combination of Poisson Variables." Quality and Reliability Engineering International 32, no. 5 (November 25, 2015): 1741–55. http://dx.doi.org/10.1002/qre.1910.

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20

van den Bosch, P. P. J., and A. E. van den Groef. "Variable Structure Control and Binary Control, a Comparison." IFAC Proceedings Volumes 25, no. 29 (October 1992): 195–200. http://dx.doi.org/10.1016/s1474-6670(17)50566-2.

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21

Rahmani, Behrooz, and Amir H. D. Markazi. "Variable Selective Control Method for Networked Control Systems." IEEE Transactions on Control Systems Technology 21, no. 3 (May 2013): 975–82. http://dx.doi.org/10.1109/tcst.2012.2194739.

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22

Zhang, Xin-fang, and Da-ping Xu. "Adaptive Fuzzy Control for Variable Speed Variable Pitch Eind Turbines." IFAC Proceedings Volumes 36, no. 20 (September 2003): 1031–36. http://dx.doi.org/10.1016/s1474-6670(17)34610-4.

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23

Boukhezzar, B., L. Lupu, H. Siguerdidjane, and M. Hand. "Multivariable control strategy for variable speed, variable pitch wind turbines." Renewable Energy 32, no. 8 (July 2007): 1273–87. http://dx.doi.org/10.1016/j.renene.2006.06.010.

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24

Lee, Heejin, Dong-Yon Kim, Taeck-Kie Lee, Sang-Hoon Kim, and Mignon Park. "Tracking Control of Variable Structure Using Fuzzy Variable Boundary Layer." Journal of Advanced Computational Intelligence and Intelligent Informatics 3, no. 4 (August 20, 1999): 332–38. http://dx.doi.org/10.20965/jaciii.1999.p0332.

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Control gain greatly affects variable structure system (VSS) performance as a system design parameter. The thin boundary layer used to eliminate chatter neighbors the sliding surface. Sliding control based on a variable boundary layer tracks better than a fixed layer. We propose variable structure control using fuzzy algorithms in control gain and the boundary layer to increase tracking efficiency, proving its feasibility in application to a simple nonlinear system.

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25

Lee, Heejin. "Tracking Control of Variable Structure System Using Variable Boundary Layer." Journal of Advanced Computational Intelligence and Intelligent Informatics 5, no. 6 (November 20, 2001): 338–45. http://dx.doi.org/10.20965/jaciii.2001.p0338.

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In this paper, a new scheme is presented for the accurate tracking control of the second-order variable structure systems using the variable boundary layer. Up to now, variable structure controller(VSC) applying the variable boundary layer did not remove chattering from an arbitrary initial state of the system trajectory because VSC has used the fixed sliding surface. But, by using the linear time-varying sliding surfaces, the scheme has the robustness against chattering from all states. The suggested method can be applied to the second-order nonlinear systems with parameter uncertainty and extraneous disturbances, and have better tracking performance than the conventional method.To demonstrate the advantages of the proposed algorithm, it is applied to a two-link manipulator.

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26

Soliman, M., O. P. Malik, and D. T. Westwick. "Fault Tolerant Control of Variable-Speed Variable-Pitch Wind Turbines: a Subspace Predictive Control Approach." IFAC Proceedings Volumes 45, no. 16 (July 2012): 1683–88. http://dx.doi.org/10.3182/20120711-3-be-2027.00117.

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27

Zeng, Yuanjing, Xiangjun Quan, Qinran Hu, Zhixiang Zou, and Fujin Deng. "State Feedback Control Based Seamless Switch Control for Microgrid Inverter." Applied Sciences 11, no. 24 (December 20, 2021): 12114. http://dx.doi.org/10.3390/app112412114.

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With the wide application of distributed generations (DGs) and microgrids (MGs), the inverter control becomes a hot research topic. For the inverter control in MG applications, first, a complex variable state-feedback-based switch control frame is proposed. In the proposed control frame, the state feedback leads to a generalized control objective (GCO), and then the instantaneous voltage and current controls are designed based on the GCO. Finally, a complex variable frequency-locked loop (FLL) is adopted to realize the voltage and current reference computation. The control system is integrated by complex variables to alleviate the seamless switch. The effectiveness of the proposed control method is validated by experimental results.

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28

Wu, Yue, Rongqiang Guan, Fang Shao, and Jingjing Yan. "A Decomposition Control of Variable Speed Control Torque Gyro." Journal of Physics: Conference Series 1744, no. 2 (February 1, 2021): 022137. http://dx.doi.org/10.1088/1742-6596/1744/2/022137.

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29

SHINADA, Koichiro, Tadashi EGAMI, and Takeshi TSUCHIYA. "Robot Trajectory Control by Preview Control with Variable Speed." Transactions of the Society of Instrument and Control Engineers 25, no. 1 (1989): 126–28. http://dx.doi.org/10.9746/sicetr1965.25.126.

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30

Filip, Ioan, Florin Dragan, Iosif Szeidert, and Adriana Albu. "Minimum-Variance Control System with Variable Control Penalty Factor." Applied Sciences 10, no. 7 (March 27, 2020): 2274. http://dx.doi.org/10.3390/app10072274.

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The present paper proposes (as the main contribution) an additional self-tuning mechanism for an adaptive minimum-variance control system, whose main goal is to extend its functionality for a large value range of unmeasurable perturbations which disturb the controlled process. Through the standard design procedure, a minimum variance controller uses by default an internal self-tuning mechanism based on the process parameter estimates. However, the main parameter which overwhelmingly influences the control performance is the control penalty factor ( ρ ) . This parameter weights the term that describes the control variance in a criterion function whose minimization is the starting point of the control law design. The classical minimum-variance control involves an off-line tuning of this parameter, its value being set as constant throughout the entire operating regime. Based on the measurement of the process output error, the contribution of the proposed strategy consists in a real-time tuning of the control penalty factor, to ensure the stability of the control system, even under conditions of high disturbances. The proposed tuning mechanism adjusts this parameter by implementing a bipositional switching strategy based on a sharp hysteresis loop. Therefore, instead of the standard solution that involves a constant value of the control penalty factor ρ (a priori computed and set), this paper proposes a dual value for this controller parameter. The main objective is to allow the controlled process to operate in a stable fashion even in more strongly disturbed regimes (regimes where the control system becomes unstable and is usually switched off for safety reasons). To validate the proposed strategy, an induction generator integrated into a wind energy conversion system was considered as controlled plant. Operating under the action of strong disturbances (wind gusts, electrical load variations), the extension of safe operating range (thus avoiding the system disengagement) is an important goal of such a control system.

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31

Truong, Dinh Quang, and Kyoung Kwan Ahn. "Robust Variable Sampling Period Control for Networked Control Systems." IEEE Transactions on Industrial Electronics 62, no. 9 (September 2015): 5630–43. http://dx.doi.org/10.1109/tie.2015.2410765.

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32

Lin, Jong‐Lick, and Hsing‐Ya Chiang. "Adaptive model following control with variable structure control system." Journal of the Chinese Institute of Engineers 11, no. 1 (January 1988): 65–72. http://dx.doi.org/10.1080/02533839.1988.9677042.

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33

Liang, Hong, Kil To Chong, Tae Soo No, and Soo-Yeong Yi. "Vehicle longitudinal brake control using variable parameter sliding control." Control Engineering Practice 11, no. 4 (April 2003): 403–11. http://dx.doi.org/10.1016/s0967-0661(02)00176-4.

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34

Schaub, Hanspeter, Srinivas R. Vadali, and John L. Junkins. "Feedback Control Law for Variable Speed Control Moment Gyros." Journal of the Astronautical Sciences 46, no. 3 (September 1998): 307–28. http://dx.doi.org/10.1007/bf03546239.

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35

Huang, Y. J. "Discrete fuzzy variable structure control for pantograph position control." Electrical Engineering (Archiv fur Elektrotechnik) 86, no. 3 (February 1, 2004): 171–77. http://dx.doi.org/10.1007/s00202-003-0200-8.

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36

Jationo, Iro, and Widarto Rachbini. "Good Governance, Komitmen Profesional dan Akuntabilitas Layanan Publik, Locus of Control Sebagai Intervening." Jurnal Riset Akuntansi & Perpajakan (JRAP) 2, no. 01 (June 8, 2015): 78–88. http://dx.doi.org/10.35838/jrap.v2i01.98.

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A B S T R A C T This research aims to empirically examine the relationship between good governance and professional commitment to accountability of public service procurement of goods/services through the government’s locus of control as an intervening variable. The population in this research are all service personnel procurement of goods/services certified government spread across 42 SKPDs (working units) in Tangerang City Government which is responsible for the procurement of goods/services of the government, as many as 70 people. In this research, variables used consisted of the dependent variable accountability of public service procurement of goods/services of the government while the independent variable is good governance and professional commitment, and locus of control variable as an intervening variable. The results showed that the governance and professional commitments do not affect the accountability. locus of control significantly influence accountability. In this study, locus of control is not a variable pemediasi on accountability procurement of goods and services. A B S T R A K Penelitian ini bertujuan untuk menguji secara empiris hubungan antara tata kelola yang baik dan komitmen profesional terhadap akuntabilitas pengadaan pelayanan publik barang/jasa melalui locus of control pemerintahan sebagai variabel intervening. Populasi dalam penelitian ini adalah seluruh tenaga pelayanan pengadaan barang/jasa pemerintah bersertifikat yang tersebar di 42 SKPD (satuan kerja) di Pemerintah Kota Tangerang yang bertanggung jawab untuk pengadaan barang/jasa pemerintah, sebanyak 70 orang. Pada penelitian ini, variabel yang digunakan terdiri dari akuntabilitas pengadaan pelayanan publik barang/jasa pemerintah sebagai variabel dependen sedangkan variabel independen adalah tata kelola yang baik dan komitmen profesional, dan locus of control sebagai variabel intervening. Hasil penelitian menunjukkan bahwa tata kelola pemerintah dan komitmen profesional tidak berpengaruh terhadap akuntabilitas. Locus of control berpengaruh signifikan terhadap akuntabilitas. Pada penelitian ini, locus of control bukan merupakan variabel pemediasi pada akuntabilitas pengadaan barang dan jasa. JEL Classification: H53

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37

Jationo, Iro, and Widarto Rachbini. "Good Governance, Komitmen Profesional dan Akuntabilitas Layanan Publik, Locus of Control Sebagai Intervening." Jurnal Riset Akuntansi & Perpajakan (JRAP) 2, no. 01 (June 8, 2015): 78–88. http://dx.doi.org/10.35838/jrap.2015.002.01.7.

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A B S T R A C T This research aims to empirically examine the relationship between good governance and professional commitment to accountability of public service procurement of goods/services through the government’s locus of control as an intervening variable. The population in this research are all service personnel procurement of goods/services certified government spread across 42 SKPDs (working units) in Tangerang City Government which is responsible for the procurement of goods/services of the government, as many as 70 people. In this research, variables used consisted of the dependent variable accountability of public service procurement of goods/services of the government while the independent variable is good governance and professional commitment, and locus of control variable as an intervening variable. The results showed that the governance and professional commitments do not affect the accountability. locus of control significantly influence accountability. In this study, locus of control is not a variable pemediasi on accountability procurement of goods and services. A B S T R A K Penelitian ini bertujuan untuk menguji secara empiris hubungan antara tata kelola yang baik dan komitmen profesional terhadap akuntabilitas pengadaan pelayanan publik barang/jasa melalui locus of control pemerintahan sebagai variabel intervening. Populasi dalam penelitian ini adalah seluruh tenaga pelayanan pengadaan barang/jasa pemerintah bersertifikat yang tersebar di 42 SKPD (satuan kerja) di Pemerintah Kota Tangerang yang bertanggung jawab untuk pengadaan barang/jasa pemerintah, sebanyak 70 orang. Pada penelitian ini, variabel yang digunakan terdiri dari akuntabilitas pengadaan pelayanan publik barang/jasa pemerintah sebagai variabel dependen sedangkan variabel independen adalah tata kelola yang baik dan komitmen profesional, dan locus of control sebagai variabel intervening. Hasil penelitian menunjukkan bahwa tata kelola pemerintah dan komitmen profesional tidak berpengaruh terhadap akuntabilitas. Locus of control berpengaruh signifikan terhadap akuntabilitas. Pada penelitian ini, locus of control bukan merupakan variabel pemediasi pada akuntabilitas pengadaan barang dan jasa. JEL Classification: H53

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38

Leonov, Rafail. "Control objects with variable transport delay." Izvestiya vysshikh uchebnykh zavedenii. Gornyi zhurnal, no. 1 (February 17, 2021): 122–30. http://dx.doi.org/10.21440/0536-1028-2021-1-122-130.

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Research aim is to analyze a class of automatic control systems at mining and concentrating facilities, where controlling action is the consumption of fuel or substance by a transporting body in order to develop recommendations on stability and control quality improvement. Research methodology consists in working out a model of automatic control system and studying the infl uence of varying transportation delay on the model, which arises in the process of stabilizing the output value of the system of automatic control. The RMS error of output value control was measured on the system’s model when measuring the delay of the main controlling action. Discussion was carried out by the example of the control process where the output value of the control object is regulated by the conveyor which feeds the ore. It has been shown that when changing the controlling action (ore consumption), time the delivery time also changes at the same time. These facilities refer to the facilities with the varying parameter – delay in control; they are poorly explored. It has been shown that control systems like that cannot adequately operate in the general case. One of the main methods of improving the quality of such control systems is to use the Smith predictor. However, in this case the system of automatic control will operate adequately under constant parameters of the control object, which is highly unlikely in the conditions of mining treatment. Delay in such systems is defi nitely associated with the speed of material feed. It has been proposed to automatically change regulator’s settings depending on the speed of the conveyor. Material can be fed using an assembly with constant speed, as an alternative, while the consumption may be carried out in accordance with the variation of the control object output value by a separate feeder

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39

Haigang Guo, and Hongxing Li. "Robust Variable Universe Adaptive Fuzzy Control." Journal of Convergence Information Technology 8, no. 6 (March 31, 2013): 1140–48. http://dx.doi.org/10.4156/jcit.vol8.issue6.136.

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40

Hunnekens, Bram, Sjors Kamps, and Nathan Van De Wouw. "Variable-Gain Control for Respiratory Systems." IEEE Transactions on Control Systems Technology 28, no. 1 (January 2020): 163–71. http://dx.doi.org/10.1109/tcst.2018.2871002.

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41

Balamurali, S., and Muhammad Aslam. "Variable batch-size attribute control chart." Journal of Statistics and Management Systems 22, no. 6 (March 22, 2019): 1037–48. http://dx.doi.org/10.1080/09720510.2018.1564207.

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42

Kuindersma, Scott R., Roderic A. Grupen, and Andrew G. Barto. "Variable risk control via stochastic optimization." International Journal of Robotics Research 32, no. 7 (June 2013): 806–25. http://dx.doi.org/10.1177/0278364913476124.

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43

Hogans IV, John E., Abdollah Homaifar, and Bijan Sayyarrodsari. "Fuzzy Inference for Variable Structure Control." Journal of Intelligent and Fuzzy Systems 2, no. 3 (1994): 229–41. http://dx.doi.org/10.3233/ifs-1994-2303.

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44

Nemir, David C., Yingjie Lin, and Roberto A. Osegueda. "Semiactive Motion Control Using Variable Stiffness." Journal of Structural Engineering 120, no. 4 (April 1994): 1291–306. http://dx.doi.org/10.1061/(asce)0733-9445(1994)120:4(1291).

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45

Kronander, Klas, and Aude Billard. "Stability Considerations for Variable Impedance Control." IEEE Transactions on Robotics 32, no. 5 (October 2016): 1298–305. http://dx.doi.org/10.1109/tro.2016.2593492.

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46

Liu, W., W. Liu, and S. K. Wei. "CMOS exponential-control variable gain amplifiers." IEE Proceedings - Circuits, Devices and Systems 151, no. 2 (2004): 83. http://dx.doi.org/10.1049/ip-cds:20040111.

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47

Feng, G., and Y. A. Jiang. "Variable structure based decentralised adaptive control." IEE Proceedings - Control Theory and Applications 142, no. 5 (September 1, 1995): 439–43. http://dx.doi.org/10.1049/ip-cta:19951883.

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48

Weibing Gao, Yufu Wang, and A. Homaifa. "Discrete-time variable structure control systems." IEEE Transactions on Industrial Electronics 42, no. 2 (April 1995): 117–22. http://dx.doi.org/10.1109/41.370376.

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49

Chih-Lyang Hwang, Chau Jan, and Ye-Hwa Chen. "Piezomechanics using intelligent variable-structure control." IEEE Transactions on Industrial Electronics 48, no. 1 (2001): 47–59. http://dx.doi.org/10.1109/41.904550.

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50

Reynolds, Marion R. "Optimal variable sampling interval control charts." Sequential Analysis 8, no. 4 (January 1989): 361–79. http://dx.doi.org/10.1080/07474948908836187.

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