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Relevant bibliographies by topics / Intelligent control systems

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Academic literature on the topic 'Intelligent control systems'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 July 2024

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Journal articles on the topic "Intelligent control systems"

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Vassilyev, S. N., A. Yu Kelina, Y. I. Kudinov, and F. F. Pashchenko. "Intelligent Control Systems." Procedia Computer Science 103 (2017): 623–28. http://dx.doi.org/10.1016/j.procs.2017.01.088.

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Zulfizar, Muratova. "INTELLIGENT CONTROL METHODS FOR STREET LIGHTING SYSTEMS." American Journal of Applied Science and Technology 3, no. 12 (December 1, 2023): 24–30. http://dx.doi.org/10.37547/ajast/volume03issue12-06.

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This comprehensive article explores the transformative journey of street lighting systems, highlighting recent advancements in intelligent control models, methods, and algorithms. The narrative encompasses the evolution from traditional, fixed-schedule lighting to dynamic, adaptive systems that respond to real-time data, sensors, and communication technologies. The article delves into the benefits, challenges, and future outlook of these innovations, emphasizing the role of machine learning, IoT integration, and specialized algorithms. It also discusses the positive impacts on energy efficiency, safety, and the overall development of smart cities.

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Dhiman, Tarun Kumar. "Intelligent Control Systems for Fault Detection and Diagnostics in Mechatronic Systems." Mathematical Statistician and Engineering Applications 70, no. 1 (January 31, 2021): 494–500. http://dx.doi.org/10.17762/msea.v70i1.2502.

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Intelligent control systems have emerged as a promising solution for fault detection and diagnostics in mechatronic systems. With the increasing complexity of modern mechatronic systems, the ability to identify and diagnose faults in real-time has become critical for ensuring efficient and reliable operation. This abstract presents an overview of intelligent control systems for fault detection and diagnostics in mechatronic systems, highlighting their key features, benefits, and applications. The main objective of intelligent control systems is to enhance the performance and robustness of mechatronic systems by continuously monitoring their behaviour and identifying potential faults. These systems leverage advanced techniques such as machine learning, artificial intelligence, and data-driven approaches to analyse the system's operational data and detect anomalies that may indicate the presence of faults. By employing intelligent algorithms, these systems can not only identify faults but also provide diagnostic information to localize and classify the detected faults. In the outcome, intelligent control systems offer significant advantages in fault detection and diagnostics for mechatronic systems. Their ability to adapt and learn from the system's behaviour, combined with advanced machine learning and data-driven techniques, enables accurate and timely detection of faults. These systems have broad applications in robotics, manufacturing, automotive, and aerospace systems, where they play a crucial role in maintaining system performance, safety, and reliability. Future research in this field will focus on improving the efficiency, scalability, and interpretability of intelligent control systems for fault detection and diagnostics in mechatronic systems.

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Ravindranathan, M., and R. Leitch. "Heterogeneous intelligent control systems." IEE Proceedings - Control Theory and Applications 145, no. 6 (November 1, 1998): 551–58. http://dx.doi.org/10.1049/ip-cta:19982397.

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Gribova, V. V., A. S. Kleshchev, and E. A. Shalfeeva. "Control of intelligent systems." Journal of Computer and Systems Sciences International 49, no. 6 (December 2010): 952–66. http://dx.doi.org/10.1134/s1064230710060134.

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Khalgui, Mohamed, and Olfa Mosbahi. "Intelligent distributed control systems." Information and Software Technology 52, no. 12 (December 2010): 1259–71. http://dx.doi.org/10.1016/j.infsof.2010.06.001.

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Prokhorov, D. V. "Intelligent Control Systems Using Computational Intelligence [book review]." IEEE Transactions on Neural Networks 18, no. 2 (March 2007): 611–12. http://dx.doi.org/10.1109/tnn.2007.893089.

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Kolková, Zuzana, Peter Hrabovský, Jozef Matušov, Martina Antošová, and Michal Holubčík. "Control and regulation systems of energy networks in buildings." MATEC Web of Conferences 168 (2018): 06005. http://dx.doi.org/10.1051/matecconf/201816806005.

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Measurement, regulation and control systems offer direct savings and reduce energy consumption, regulating heating, cooling, ventilation or lighting in the intelligent buildings. They provide quick and accurate information on the status of regulated systems or possible malfunction. Systems can use the special meteorological stations to get information about wind velocity and direction, outdoor temperature, intensity and direction of sunlight. They respond flexibly to changes in external parameters. Intelligent buildings combine architecture and aesthetics of the construction, safety, comfort and quality of the living. These buildings are productive, energy efficient and environmentally acceptable. Intelligent buildings combine internal and external intelligence building, intelligence used materials and constructions. The most important aspect is the cooperation of people with those systems. Intelligent buildings should be permanent, healthy, technologically advanced.

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Ismayil Ismayilov, Ismayil Ismayilov, and Izzet Orujov Izzet Orujov. "BASIC PRINCIPLES OF BUILDING INTELLIGENT SYSTEMS FLIGHT CONTROL." ETM - Equipment, Technologies, Materials 16, no. 04 (October 6, 2023): 14–19. http://dx.doi.org/10.36962/etm16042023-14.

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The issues of creating intelligent control systems in a dynamically changing flight environment are considered and the principle of constructing an onboard intelligent flight control system is proposed. The implementation of the proposed system is based on the pilot's situational awareness of the flight progress in real time, which allows avoiding errors and maintaining enhanced synergy between human and avionics systems. Constant adaptation of the cockpit, as well as onboard. automation successfully maintains the pilot's workload within an optimal range, mitigating the occurrence of dangerous levels of fatigue. Methods for assessing and predicting the threat of an aviation accident based on direct control of the variation in the values of characteristics that affect flight safety, using methods and tools of artificial intelligence, are proposed. Keywords: flight safety, aircraft, intelligent aviation system, accident threat, prevention, control system, artificial intelligence, aircraft control systems

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Ragg, C., T. Jungeblut, and B. Jurke. "Intelligente Werkzeugmaschinen/Intelligent tool machines." wt Werkstattstechnik online 105, no. 05 (2015): 252–56. http://dx.doi.org/10.37544/1436-4980-2015-05-4.

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Dieser Fachbeitrag beschreibt das optimierte Einrichten eines Bearbeitungsprozesses auf einer Werkzeugmaschine. Eine „intelligente“ Messtechnik erkennt die Spannsituation und bestimmt autonom den exakten Nullpunkt, gefolgt von einer realitätsnahen Simulation zum Validieren des auszuführenden NC-Programms. Dieser Ansatz steht im Fokus des Forschungsprojekts „Intelligente Werkzeugmaschine“ (iWZM) im Rahmen des Spitzenclusters „Intelligente Technische Systeme OstWestfalenLippe“ (it’s OWL).   This technical paper describes an optimized set up of a machining process on a tool machine. An intelligent measuring method detects the clamping situation and determines autonomously the exact zero point followed by a realistic simulation that validates the NC program. This approach is focussed by the research project “Intelligent Machine Tool“ (iWZM), which is part of the government-financed project “Intelligent Technical Systems OstWestfalenLippe“ (it’s OWL).

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Dissertations / Theses on the topic "Intelligent control systems"

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Ravindranathan, Mohan Das K. "Heterogeneous intelligent control systems." Thesis, Heriot-Watt University, 1996. http://hdl.handle.net/10399/736.

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GHAFFARI, MASOUD. "PERCEPTION-BASED CONTROL FOR INTELLIGENT SYSTEMS." University of Cincinnati / OhioLINK, 2006. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1155739459.

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Hu, Yuebing, and 胡跃冰. "Consensus control of multi-agent systems." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 2011. http://hub.hku.hk/bib/B4658657X.

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Houghton, Michael. "A transputer network for intelligent control systems." Thesis, University of Liverpool, 1995. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.321150.

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Teltz, Richard W. "Open architecture control for intelligent machining systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1998. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape11/PQDD_0006/NQ42883.pdf.

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Abdullah, Rudwan Ali Abolgasim. "Intelligent methods for complex systems control engineering." Thesis, University of Stirling, 2007. http://hdl.handle.net/1893/257.

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This thesis proposes an intelligent multiple-controller framework for complex systems that incorporates a fuzzy logic based switching and tuning supervisor along with a neural network based generalized learning model (GLM). The framework is designed for adaptive control of both Single-Input Single-Output (SISO) and Multi-Input Multi-Output (MIMO) complex systems. The proposed methodology provides the designer with an automated choice of using either: a conventional Proportional-Integral-Derivative (PID) controller, or a PID structure based (simultaneous) Pole and Zero Placement controller. The switching decisions between the two nonlinear fixed structure controllers is made on the basis of the required performance measure using the fuzzy logic based supervisor operating at the highest level of the system. The fuzzy supervisor is also employed to tune the parameters of the multiple-controller online in order to achieve the desired system performance. The GLM for modelling complex systems assumes that the plant is represented by an equivalent model consisting of a linear time-varying sub-model plus a learning nonlinear sub-model based on Radial Basis Function (RBF) neural network. The proposed control design brings together the dominant advantages of PID controllers (such as simplicity in structure and implementation) and the desirable attributes of Pole and Zero Placement controllers (such as stable set-point tracking and ease of parameters’ tuning). Simulation experiments using real-world nonlinear SISO and MIMO plant models, including realistic nonlinear vehicle models, demonstrate the effectiveness of the intelligent multiple-controller with respect to tracking set-point changes, achieve desired speed of response, prevent system output overshooting and maintain minimum variance input and output signals, whilst penalising excessive control actions.

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Van, Dam Catharina Johanna Henrica. "Intelligent control of radio communication system." Thesis, Queen Mary, University of London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299184.

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Su, Jiancheng. "Component-based Intelligent Control Architecture for Reconfigurable Manufacturing Systems." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/29980.

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The present dynamic manufacturing environment has been characterized by a greater variety of products, shorter life-cycles of products and rapid introduction of new technologies, etc. Recently, a new manufacturing paradigm, i.e. Reconfigurable Manufacturing Systems (RMS), has emerged to address such challenging issues. RMSs are able to adapt themselves to new business conditions timely and economically with a modular design of hardware/software system. Although a lot of research has been conducted in areas related to RMS, very few studies on system-level control for RMS have been reported in literature. However, the rigidity of current manufacturing systems is mainly from their monolithic design of control systems. Some new developments in Information Technology (IT) bring new opportunities to overcome the inflexibility that shadowed control systems for years. Component-based software development gains its popularity in 1990â s. However, some well-known drawbacks, such as complexity and poor real-time features counteract its advantages in developing reconfigurable control system. New emerging Extensible Markup Language (XML) and Web Services, which are based on non-proprietary format, can eliminate the interoperability problems that traditional software technologies are incompetent to accomplish. Another new development in IT that affects the manufacturing sector is the advent of agent technology. The characteristics of agent-based systems include autonomous, cooperative, extendible nature that can be advantageous in different shop floor activities. This dissertation presents an innovative control architecture, entitled Component-based Intelligent Control Architecture (CICA), designed for system-level control of RMS. Software components and open-standard integration technologies together are able to provide a reconfigurable software structure, whereas agent-based paradigm can add the reconfigurability into the control logic of CICA. Since an agent-based system cannot guarantee the best global performance, agents in the reference architecture are used to be exception handlers. Some widely neglected problems associated with agent-based system such as communication load and local interest conflicts are also studied. The experimental results reveal the advantage of new agent-based decision making system over the existing methodologies. The proposed control system provides the reconfigurability that lacks in current manufacturing control systems. The CICA control architecture is promising to bring the flexibility in manufacturing systems based on experimental tests performed.
Ph. D.

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Wong, Kwok Wai Johnny. "Development of selection evaluation and system intelligence analytic models for the intelligent building control systems." Thesis, The Hong Kong Polytechnic University, 2007. https://eprints.qut.edu.au/20343/1/c20343.pdf.

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With the availability of innumerable ‘intelligent’ building products and the dearth of inclusive evaluation tools, design teams are confronted with the quandary of choosing the apposite building control systems to suit the needs of a particular intelligent building project. The paucity of measures that represent the degree of system intelligence and indicate the desirable goal in intelligent building control systems design further inhibits the consumers from comparing numerous products from the viewpoint of intelligence. This thesis is organised respectively to develop models for facilitating the selection evaluation and the system intelligence analysis for the seven predominant building control systems in the intelligent building. To achieve these objectives, systematic research activities are conducted to first develop, test and refine the general conceptual models using consecutive surveys; then, to convert the developed conceptual frameworks to the practical models; and, finally, to evaluate the effectiveness of the practical models by means of expert validations.----- The findings of this study, on one hand, suggest that there are different sets of critical selection criteria (CSC) affecting the selection decision of the intelligent building control systems. Service life, and operating and maintenance costs are perceived as two common CSC. The survey results generally reflect that an ‘intelligent’ building control system does not necessarily need to be technologically advanced. Instead, it should be the one that can ensure efficiency and enhance user comfort and cost effectiveness. On the other hand, the findings of the research on system intelligence suggest that each building control system has a distinctive set of intelligence attributes and indicators. The research findings also indicate that operational benefits of the intelligent building exert a considerable degree of influence on the relative importance of intelligence indicators of the building control systems in the models. This research not only presents a systematic and structured approach to evaluate candidate building control systems against the CSC, but it also suggests a benchmark to measure the degree of intelligence of one control system candidate against another.

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Rufus, Freeman Jr. "Intelligent approaches to mode transition control." Diss., Georgia Institute of Technology, 2000. http://hdl.handle.net/1853/13281.

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Books on the topic "Intelligent control systems"

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Wen, John T., and Sandipan Mishra, eds. Intelligent Building Control Systems. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-68462-8.

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E, Ruano A., and Institution of Electrical Engineers, eds. Intelligent control systems using computational intelligence techniques. London: Institution of Electrical Engineers, 2005.

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Wilamowski, Bogdan M. Intelligent systems. 2nd ed. Boca Raton: CRC Press, 2011.

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Pham, Duc Truong. Intelligent Quality Systems. London: Springer London, 1996.

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Katic, Dusko, and Miomir Vukobratovic. Intelligent Control of Robotic Systems. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-017-0317-8.

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Dote, Yasuhiko. Intelligent control: Power electronic systems. Oxford [England]: Oxford University Press, 1998.

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Katic, Dusko. Intelligent Control of Robotic Systems. Dordrecht: Springer Netherlands, 2003.

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Miomir, Vukobratović, ed. Intelligent control of robotic systems. Dordrecht: Kluwer Academic Publishers, 2003.

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International, Conference on Intelligent Autonomous Systems (7th 2002 Marina del Rey Calif ). Intelligent autonomous systems 7. Amsterdam: IOS Press, 2002.

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Engineers, Society of Automotive, and Society of Automotive Engineers. World Congress, eds. Intelligent vehicle systems. Warrendale, PA: Society of Automotive Engineers, 2000.

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Book chapters on the topic "Intelligent control systems"

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Hamano, Fumio. "Intelligent Control." In Expert Systems and Robotics, 737–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/978-3-642-76465-3_45.

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Westphal, Louis C. "Intelligent control." In Handbook of Control Systems Engineering, 841–94. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1533-3_35.

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de Silva, Clarence W. "Intelligent Control." In Encyclopedia of Complexity and Systems Science, 1–33. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-3-642-27737-5_288-2.

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de Silva, Clarence W. "Intelligent Control." In Encyclopedia of Complexity and Systems Science, 4868–91. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-30440-3_288.

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De Winter, Marylene, and Marcel Rijckaert. "Intelligent Process Control." In Failsafe Control Systems, 217–27. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-009-0429-3_16.

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Li, Zhijun, Chenguang Yang, and Liping Fan. "Intelligent Control." In Advanced Control of Wheeled Inverted Pendulum Systems, 127–73. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-2963-9_7.

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Zile, Mehmet. "Intelligent and Adaptive Control." In Power Systems, 423–46. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23723-3_17.

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Merzouki, Rochdi, Arun Kumar Samantaray, Pushparaj Mani Pathak, and Belkacem Ould Bouamama. "Robust Overwhelming Control and Impedance Control." In Intelligent Mechatronic Systems, 683–702. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-4628-5_9.

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Shaw, Ian S. "Collaborative Intelligent Control Systems." In Fuzzy Control of Industrial Systems, 181–86. Boston, MA: Springer US, 1998. http://dx.doi.org/10.1007/978-1-4757-2813-2_13.

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Shoureshi, R., M. Wheeler, and L. Brackney. "Applied Intelligent Control Systems." In Artificial Intelligence in Industrial Decision Making, Control and Automation, 101–26. Dordrecht: Springer Netherlands, 1995. http://dx.doi.org/10.1007/978-94-011-0305-3_5.

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Conference papers on the topic "Intelligent control systems"

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Nawrocka, Agata, and Marcin Nawrocki. "Intelligent control systems." In 2018 19th International Carpathian Control Conference (ICCC). IEEE, 2018. http://dx.doi.org/10.1109/carpathiancc.2018.8399623.

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"TT intelligent control systems." In 2018 15th International Workshop on Advanced Motion Control (AMC). IEEE, 2018. http://dx.doi.org/10.1109/amc.2019.8371081.

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Gorobchenko, Stanislav, Dmitriy Kovalev, Sergey Voinash, Ramil Zagidullin, Stanislav Akhmetshin, and Aleksey Teppoev. "Intelligent climate control systems." In Third International Conference on Optics, Computer Applications, and Materials Science (CMSD-III 2023), edited by Shahriyor Sadullozoda and Ramazona Abdullozoda. SPIE, 2024. http://dx.doi.org/10.1117/12.3024944.

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Shweta A.S. "Intelligent refrigerator using ARTIFICIAL INTELLIGENCE." In 2017 11th International Conference on Intelligent Systems and Control (ISCO). IEEE, 2017. http://dx.doi.org/10.1109/isco.2017.7856036.

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Westermann, Thorsten, Harald Anacker, Roman Dumitrescu, Ursula Frank, and Axel Hessenkämper. "Interdisciplinary System Architecture for Intelligent Technical Systems." In ASME 2015 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/imece2015-50579.

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Nowadays, mechanical engineering products change from mechatronic products, characterized by the close interplay of mechanics, electronics, control and software engineering to Intelligent Technical Systems (ITS). Intelligent Technical Systems are mechatronic systems with inherent partial intelligence. Mechanical engineering products will be a symbiosis of information technology and mechanical parts. This will entail a rising complexity of technical systems and its development. Therefore we introduce an approach to improve system architectures of mechanical engineering products to realize Intelligent Technical Systems. Our approach meets the following challenges: creating a domain-spanning description of the system with Model-Based Systems Engineering (MBSE); designing an interdisciplinary and modular system architecture by using the technological concept of Intelligent Technical Systems; identifying and implementing solution elements as reusable modules. Validated by a separator, our results clarify the benefits of MBSE and the technological concept of ITS to improve system architectures of mechanical engineering products to realize Intelligent Technical Systems.

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Rybina, Galina V., and Victor M. Rybin. "New intelligent control systems architectures based on dynamic intelligent systems." In 2016 IEEE 8th International Conference on Intelligent Systems (IS). IEEE, 2016. http://dx.doi.org/10.1109/is.2016.7737453.

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Baranov, L. A., Yongxian Zhang, and A. E. Ermakova. "MODELS OF TRAIN PARKING DURATIONS FOR INTELLIGENT CENTRALIZED CONTROL SYSTEMS." In Intelligent transport systems. Russian University of Transport, 2024. http://dx.doi.org/10.30932/9785002446094-2024-39-42.

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The role of models of train parking durations in intelligent metro train traffic control systems is shown. The classification of parking duration models and their analysis are given. Deterministic, stochastic, and combined models are considered. The application of models with the prediction of disturbances is justified. The efficiency of using intelligent extrapolators of disturbances in real-time control systems is shown. The features of using the results of forecasting disturbances in intelligent centralized metro train traffic control systems are considered.

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"Intelligent control for mechatronic systems." In IECON 2011 - 37th Annual Conference of IEEE Industrial Electronics. IEEE, 2011. http://dx.doi.org/10.1109/iecon.2011.6120008.

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"Intelligent Learning in Control Systems." In 2019 IEEE International Conference on Industrial Cyber Physical Systems (ICPS). IEEE, 2019. http://dx.doi.org/10.1109/icphys.2019.8780278.

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Fujinaka, Toru, and Sigeru Omatu. "Network-Based Intelligent Control Systems." In 2008 International Conference on Computational Intelligence for Modelling Control & Automation. IEEE, 2008. http://dx.doi.org/10.1109/cimca.2008.105.

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Reports on the topic "Intelligent control systems"

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Morse, A. S. Intelligent Control of Uncertain Systems. Fort Belvoir, VA: Defense Technical Information Center, March 2000. http://dx.doi.org/10.21236/ada375427.

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Quintero, Richard. A real-time control system methodology for developing intelligent control systems. Gaithersburg, MD: National Institute of Standards and Technology, 1992. http://dx.doi.org/10.6028/nist.ir.4936.

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Jannett, Thomas C. Intelligent Control for Future Autonomous Distributed Sensor Systems. Fort Belvoir, VA: Defense Technical Information Center, March 2007. http://dx.doi.org/10.21236/ada468584.

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Chaudhary, Anil B. Intelligent Control Systems for Hot Forging and Extrusion Processes. Fort Belvoir, VA: Defense Technical Information Center, November 1994. http://dx.doi.org/10.21236/ada411076.

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Mitter, Sanjoy K. Center for the Study of Intelligent Control Systems (Fellowships). Fort Belvoir, VA: Defense Technical Information Center, March 1993. http://dx.doi.org/10.21236/ada263604.

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Pertzborn, Amanda J., and Daniel A. Veronica. Baseline Control Systems in the Intelligent Building Agents Laboratory. National Institute of Standards and Technology, September 2022. http://dx.doi.org/10.6028/nist.tn.2178.

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McClure, Donald E. Center for Intelligent Control Systems, Brown University Component: Graduate Research Fellowships. Fort Belvoir, VA: Defense Technical Information Center, August 1991. http://dx.doi.org/10.21236/ada253620.

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Aman Behal, Sunil Kumar, and Goodarz Ahmadi. Intelligent Control via Wireless Sensor Networks for Advanced Coal Combustion Systems. Office of Scientific and Technical Information (OSTI), August 2007. http://dx.doi.org/10.2172/934579.

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Ostrum, Lee, and Milos Manic. Demonstrating Hybrid Heat Transport and Energy Conversion System Performance Characterization Using Intelligent Control Systems. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1407694.

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Albus, James Sacra. Concept for a reference model architecture for real-time intelligent control systems :. Gaithersburg, MD: National Bureau of Standards, 1990. http://dx.doi.org/10.6028/nist.tn.1277.

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