Relevant bibliographies by topics / Fuzzy-logic control

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Fuzzy-logic control'

Author: Grafiati
Published: 4 June 2021
Last updated: 25 July 2025

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Journal articles on the topic "Fuzzy-logic control"

1

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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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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Lee, C. C. "Fuzzy logic in control systems: fuzzy logic controller. I." IEEE Transactions on Systems, Man, and Cybernetics 20, no. 2 (1990): 404–18. http://dx.doi.org/10.1109/21.52551.

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Lee, C. C. "Fuzzy logic in control systems: fuzzy logic controller. II." IEEE Transactions on Systems, Man, and Cybernetics 20, no. 2 (1990): 419–35. http://dx.doi.org/10.1109/21.52552.

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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 st
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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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Africa, Aaron Don M. "Fuzzy Logic Temperature Control: A feedback control system implemented by fuzzy logic." International Journal of Emerging Trends in Engineering Research 8, no. 5 (2020): 1879–85. http://dx.doi.org/10.30534/ijeter/2020/66852020.

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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 (2018): 2190–93. http://dx.doi.org/10.31142/ijtsrd18263.

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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 (2016): 56–61. http://dx.doi.org/10.9756/bijrce.8201.

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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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Dissertations / Theses on the topic "Fuzzy-logic control"

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Hoyle, W. J. "Fuzzy logic, control and optimisation." Thesis, University of Canterbury. Mechanical Engineering, 1996. http://hdl.handle.net/10092/6458.

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This thesis examines the utility of fuzzy logic in the field of control engineering. A tutorial introduction to the field of fuzzy control is presented during the development of an efficient fuzzy controller. Using the controller as a starting point, a set of criteria are developed that ensure a close connection between rule base construction and control surface geometry. The properties of the controller are exploited in the design of a global controller optimiser based on a genetic algorithm, and a tutorial explaining how the optimiser may be used to effect automatic controller design is give
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Ali, Agha Rehmat. "Predicted Speed Control based on Fuzzy Logic for Belt Conveyors : Fuzzy Logic Control for Belt Conveyors." Thesis, Karlstads universitet, Avdelningen för fysik och elektroteknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:kau:diva-70106.

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In order to achieve energy savings for belt conveyor system, speed control provides one of the best solutions. Most of the traditional belt conveyors used in the industries are based on constant speed for all operational times. Due to the need and advancements in technology, Variable Frequency Drives (VFD) are employed in industries for a number of processes. Passive Speed Control was previously suggested for the proper utilization of VFD to make belt conveyor systems more power e- cient with increased life expectancy and reduced environmental eects including the noise reduction caused by cons
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Farah, Hassan. "The fuzzy logic control of aircraft." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape8/PQDD_0003/MQ43339.pdf.

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Marriott, Jack. "Adaptive robust fuzzy logic control design." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/15819.

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Wang, Jian Zhou. "Robust control with fuzzy logic algorithms." Thesis, University of Edinburgh, 1997. http://hdl.handle.net/1842/13195.

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This thesis presents the results of an investigation of the robustness of the widely used Mandani-type fuzzy logic control systems under a wide variation of parameters of the controlled process. The measurements of the dynamic performance and system robustness of a control system were firstly defined from the engineering point of view, and the concepts of the robust space and the robustness index were introduced. The robustness of the FLC systems was investigated by analyzing the structure of the fuzzy rule base and membership functions of the input-output variables. Based on the close relatio
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Cook, Brandon M. "Multi-Agent Control Using Fuzzy Logic." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447688633.

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Farah, Hassan (Hassan Kahiye) Carleton University Dissertation Engineering Mechanical and Aerospace. "The Fuzzy logic control of aircraft." Ottawa, 1999.

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García, Z. Yohn E. "Fuzzy logic in process control: A new fuzzy logic controller and an improved fuzzy-internal model controller." Scholar Commons, 2006. http://scholarcommons.usf.edu/etd/2529.

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Two fuzzy controllers are presented. A fuzzy controller with intermediate variable designed for cascade control purposes is presented as the FCIV controller. An intermediate variable and a new set of fuzzy logic rules are added to a conventional Fuzzy Logic Controller (FLC) to build the Fuzzy Controller with Intermediate Variable (FCIV). The new controller was tested in the control of a nonlinear chemical process, and its performance was compared to several other controllers. The FCIV shows the best control performance regarding stability and robustness. The new controller also has an acceptab
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García, Z. Yohn E. "Fuzzy logic in process control : a new fuzzy logic controller and an improved fuzzy-internal model controller." [Tampa, Fla] : University of South Florida, 2006. http://purl.fcla.edu/usf/dc/et/SFE0001552.

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Bell, Michael Ray. "Fuzzy logic control of uncertain industrial processes." Thesis, Georgia Institute of Technology, 1996. http://hdl.handle.net/1853/18998.

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Books on the topic "Fuzzy-logic control"

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Driankov, Dimiter, Peter W. Eklund, and Anca L. Ralescu, eds. Fuzzy Logic and Fuzzy Control. Springer Berlin Heidelberg, 1994. http://dx.doi.org/10.1007/3-540-58279-7.

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Jager, René. Fuzzy logic in control. Techn. Univ, 1995.

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McNeill, Daniel. Fuzzy logic. Simon & Schuster, 1993.

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Silva, Clarence W. De. Intelligent control: Fuzzy logic applications. CRC Press, 1995.

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United States. National Aeronautics and Space Administration., ed. Learning fuzzy logic control system. National Aeronautics and Space Administration, 1994.

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G, Chen. Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. CRC Press, 2000.

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Tat, Pham Trung, ed. Introduction to fuzzy sets, fuzzy logic, and fuzzy control systems. CRC Press, 2001.

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Dimiter, Driankov, Eklund Peter W. 1962-, and Ralescu Anca L. 1949-, eds. Fuzzy logic and fuzzy control: IJCAI '91 workshops on fuzzy logic and fuzzy control, Sydney, Australia, August 24, 1991 : proceedings. Springer-Verlag, 1994.

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Espinosa, Jairo, Joos Vandewalle, and Vincent Wertz. Fuzzy Logic, Identification and Predictive Control. Springer London, 2005. http://dx.doi.org/10.1007/b138626.

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Yen, John. Fuzzy logic: Intelligence, control, and information. Prentice Hall, 1999.

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Book chapters on the topic "Fuzzy-logic control"

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Glorennec, Pierre Yves. "Adaptive Fuzzy Control." In Fuzzy Logic. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_50.

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Palm, R. "On the Compatibility of Fuzzy Control and Conventional Control Techniques." In Fuzzy Logic. Vieweg+Teubner Verlag, 1996. http://dx.doi.org/10.1007/978-3-322-88955-3_3.

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Bien, Zeungnam, and Heegyoo Lee. "Time Weighted Fault Tolerant Control." In Fuzzy Logic. Springer Netherlands, 1993. http://dx.doi.org/10.1007/978-94-011-2014-2_47.

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Trillas, Enric, and Luka Eciolaza. "An Introduction to Fuzzy Control." In Fuzzy Logic. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-14203-6_8.

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Gerla, Giangiacomo. "Fuzzy Control and Approximate Reasoning." In Fuzzy Logic. Springer Netherlands, 2001. http://dx.doi.org/10.1007/978-94-015-9660-2_10.

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Protzel, P., R. Holve, J. Bemasch, and K. Naab. "Abstandsregelung von Fahrzeugen mit Fuzzy Control." In Fuzzy Logic. Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-78694-5_22.

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Lloyd, Nathan, and Arjab Singh Khuman. "Adaptive Cruise Control Using Fuzzy Logic." In Fuzzy Logic. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66474-9_12.

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Ben-Ari, Mordechai, and Francesco Mondada. "Fuzzy Logic Control." In Elements of Robotics. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62533-1_11.

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D’Ambrosio, Bruce. "Fuzzy Logic Control." In Qualitative Process Theory Using Linguistic Variables. Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4613-9671-0_2.

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Mokhtari, Mohand, and Michel Marie. "Fuzzy logic control." In Engineering Applications of MATLAB® 5.3 and SIMULINK® 3. Springer London, 2000. http://dx.doi.org/10.1007/978-1-4471-0741-5_3.

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Conference papers on the topic "Fuzzy-logic control"

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Kumar, Ramesh, Mrinal Dutta, and Sachit Rathee. "Solar photovoltaic MPPT analysis using PO-fuzzy logic controller and I-C fuzzy logic controller." In 2024 International Conference on Control, Computing, Communication and Materials (ICCCCM). IEEE, 2024. https://doi.org/10.1109/iccccm61016.2024.11039985.

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Bonivento, Claudio, Cesare Fantuzzi, and Riccardo Rovatti. "Fuzzy Logic Control." In Proceedings of the International Summer School. WORLD SCIENTIFIC, 1998. http://dx.doi.org/10.1142/9789814528450.

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Kim, Sunghwan, and William W. Clark. "Fuzzy Logic Semi-Active Vibration Control." In ASME 1999 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/imece1999-0564.

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Abstract Fuzzy logic control is so-called artificial intelligence because control laws are designed by human decision, not by deterministic numerical calculations. This paper investigates the effectiveness of a fuzzy logic control method in vibration control with a semi-active MR fluid actuator. The system to be controlled is a simply supported beam to which is applied a persistent excitation. Numerical simulation and experimental tests are performed. Three semi-active control laws, one deterministic and two fuzzy-logic, were applied and compared on the basis of vibration control performance a
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Kang, H., and G. Vachtsevanos. "Adaptive fuzzy logic control." In IEEE International Conference on Fuzzy Systems. IEEE, 1992. http://dx.doi.org/10.1109/fuzzy.1992.258648.

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Trollope, James E., Leszek Koszalka, Iwona Pozniak-Koszalka, and Keith J. Burnham. "A fuzzy logic approach for vehicle collision energy distribution." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915159.

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Zhao, Zhen-Yu, Masayoshi Tomizuka, and Setsuo Sagara. "A Fuzzy Tuner for Fuzzy Logic Controllers." In 1992 American Control Conference. IEEE, 1992. http://dx.doi.org/10.23919/acc.1992.4792541.

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Simon, Andra´s, and George T. Flowers. "Magnetic Bearing Control Using Interval Type-2 Fuzzy Logic." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82507.

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Magnetic bearings are an exciting and innovative technology that has seen considerable advances in recent years. Being unstable by nature, these systems require active control. Most often linear techniques are used very successfully. On the other hand, there are applications where linear methods have limited effectiveness. Fuzzy logic control performs very well in nonlinear control situations where the plant parameters are either partially or mostly unidentified. Its effectiveness for nonlinear systems also offers advantages to magnetic bearing systems. Type-2 fuzzy logic systems represent sig
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Edwards, Dean B., and John R. Canning. "An Algorithm for Designing Conventional and Fuzzy Logic Control Systems." In ASME 1997 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/detc97/cie-4465.

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Abstract This paper presents an algorithm that can be used to design either conventional or fuzzy logic control systems. In order to use the algorithm, the engineer must first choose a performance index for the system which he or she wants to optimize relative to some specified design parameters. For conventional state space controllers, the design parameters are the feedback constants associated with the state variables of the system. For fuzzy logic controllers, the design parameters are the parameters used to define the fuzzy sets for the input state and control variables. We use the algori
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Lieh, Junghsen, and Wei Jie Li. "Fuzzy Logic Control of Material Forming Process." In ASME 1997 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/imece1997-0409.

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Abstract Adaptive controllers with self-adjustment capabilities possess many advantages over conventional methods. An adaptive fuzzy controller may be implemented either in a direct form or in an indirect form, and it is generally referred to as a self-organizing controller. One advantage of fuzzy controllers is their simple computation requirements in comparison with more algorithmic-based controllers. A metal forming process is a good candidate for the implementation of fuzzy logic rules because of its nonlinear and stochastic properties. In this paper, a prototype electromechanical forming
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Coban, Melih, and Murat Fidan. "Fuzzy Logic Based Temperature Control." In 2019 3rd International Symposium on Multidisciplinary Studies and Innovative Technologies (ISMSIT). IEEE, 2019. http://dx.doi.org/10.1109/ismsit.2019.8932906.

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Reports on the topic "Fuzzy-logic control"

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Combs, James E. Advanced Control Techniques with Fuzzy Logic. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada604019.

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Almufti, Ali. Parallel Hybrid Vehicles using Fuzzy Logic Control. Defense Technical Information Center, 2009. http://dx.doi.org/10.21236/ada513229.

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Lawson, J. E., M. G. Bell, R. J. Marsala, and D. Mueller. Beta normal control of TFTR using fuzzy logic. Office of Scientific and Technical Information (OSTI), 1994. http://dx.doi.org/10.2172/10182059.

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Tang, Yu, and Kung C. Wu. Active structural control by fuzzy logic rules: An introduction. Office of Scientific and Technical Information (OSTI), 1996. http://dx.doi.org/10.2172/448036.

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Tang, Y. Active structural control by fuzzy logic rules: An introduction. Office of Scientific and Technical Information (OSTI), 1995. http://dx.doi.org/10.2172/123263.

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Rajagopalan, A., G. Washington, G. Rizzoni, and Y. Guezennec. Development of Fuzzy Logic and Neural Network Control and Advanced Emissions Modeling for Parallel Hybrid Vehicles. Office of Scientific and Technical Information (OSTI), 2003. http://dx.doi.org/10.2172/15006009.

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Zyikin, P. V. Adjusting the roughness of the part surface by means of a fuzzy logic-based longitudinal feed control system. Ailamazyan Program Systems Institute of Russian Academy of Sciences, 2024. http://dx.doi.org/10.12731/ofernio.2023.25270.

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Zyikin, P. V. Adjusting the roughness of the part surface by means of a fuzzy logic-based longitudinal feed control system. Ailamazyan Program Systems Institute of Russian Academy of Sciences, 2024. http://dx.doi.org/10.12731/ofernio.2024.25284.

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Willson. L51756 State of the Art Intelligent Control for Large Engines. Pipeline Research Council International, Inc. (PRCI), 1996. http://dx.doi.org/10.55274/r0010423.

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Computers have become a vital part of the control of pipeline compressors and compressor stations. For many tasks, computers have helped to improve accuracy, reliability, and safety, and have reduced operating costs. Computers excel at repetitive, precise tasks that humans perform poorly - calculation, measurement, statistical analysis, control, etc. Computers are used to perform these type of precise tasks at compressor stations: engine / turbine speed control, ignition control, horsepower estimation, or control of complicated sequences of events during startup and/or shutdown. For other task
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