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Journal articles on the topic 'Intelligent control systems'

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Published: 4 June 2021
Last updated: 29 July 2024

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1

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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2

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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3

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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4

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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5

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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7

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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8

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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10

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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11

de Pablo, E., A. Jimenez, V. López, and P. Rodríguez. "Real-Time Intelligent Control Systems." Integrated Computer-Aided Engineering 2, no. 3 (July 1, 1995): 163–64. http://dx.doi.org/10.3233/ica-1995-2301.

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12

Meytus, Volodymyr Yu. "Intelligent Control of Production Systems." Control Systems and Computers, no. 4 (294) (November 2021): 19–27. http://dx.doi.org/10.15407/csc.2021.04.019.

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Introduction. The research is devoted to the problem of building intelligent production management systems. They allow organizing modern efficient production without human participation in its management. Problem. To develop a scheme for creating an algorithm underlying the creation of an intelligent production management system, using the example of a mechanical workshop of a machine-building enterprise. Purpose. To present a scheme for constructing an intelligent control of management system (workshop), which is developed based on the application of an intelligent modeling algorithm and the use of a set of knowledge about the subject area. Methods. The main method used in the development of an intelligent control system is the method of intelligent modeling, which is applied to the knowledge of the machine-building enterprise workshop. Result. A diagram of an intelligent shop management system is built based on the use of knowledge that describes this shop. The model of the workshop is proposed. It consists of a system of plans describing the work of the workshop. Not only the production process is planned, but also material and technical supply, resources, and financial management. Plans are built using the scheduling theory. Conclusion. An intelligent management system at an enterprise implies the possibility of management without human participation in the management process. This system is based on obtaining information about the state of all elements of business processes based on the use of cyber-physical systems and the Internet of things.
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13

LEE, T. H., and C. C HANG. "Guest Editorial: Intelligent control systems." International Journal of Systems Science 29, no. 7 (July 1998): 677. http://dx.doi.org/10.1080/00207729808929562.

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14

Dote, Y., and R. G. Halt. "Intelligent control, power electronic systems." IEEE Power Engineering Review 19, no. 9 (September 1999): 44. http://dx.doi.org/10.1109/mper.1999.785805.

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15

Harazov, Viktor. "INTELLIGENT DEVICES AND CONTROL SYSTEMS." Bulletin of the Saint Petersburg State Institute of Technology (Technical University) 26, no. 52 (December 2014): 92–94. http://dx.doi.org/10.15217/issn19984-9.2014.26.92.

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16

Blair, D. D., D. L. Jensen, D. R. Doan, and T. K. Kim. "Networked intelligent motor-control systems." IEEE Industry Applications Magazine 7, no. 6 (2001): 18–25. http://dx.doi.org/10.1109/2943.959112.

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17

Liu, T. I., E. J. Ko, and J. Lee. "Intelligent control of dynamic systems." Journal of the Franklin Institute 330, no. 3 (May 1993): 491–503. http://dx.doi.org/10.1016/0016-0032(93)90095-c.

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18

Lennon, W. K., and K. M. Passino. "Intelligent control for brake systems." IEEE Transactions on Control Systems Technology 7, no. 2 (March 1999): 188–202. http://dx.doi.org/10.1109/87.748145.

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19

Tugengol’d, A. K., E. A. Luk’yanov, E. V. Remizov, and O. E. Korotkov. "Intelligent control of technological systems." Russian Engineering Research 28, no. 5 (May 2008): 479–84. http://dx.doi.org/10.3103/s1068798x08050158.

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20

Passino, K. M. "Intelligent control for autonomous systems." IEEE Spectrum 32, no. 6 (June 1995): 55–62. http://dx.doi.org/10.1109/6.387144.

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21

Grobelna, Iwona. "Intelligent Industrial Process Control Systems." Sensors 23, no. 15 (August 1, 2023): 6838. http://dx.doi.org/10.3390/s23156838.

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22

Wilson, Callum, Francesco Marchetti, Marilena Di Carlo, Annalisa Riccardi, and Edmondo Minisci. "Classifying Intelligence in Machines: A Taxonomy of Intelligent Control." Robotics 9, no. 3 (August 21, 2020): 64. http://dx.doi.org/10.3390/robotics9030064.

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The quest to create machines that can solve problems as humans do leads us to intelligent control. This field encompasses control systems that can adapt to changes and learn to improve their actions—traits typically associated with human intelligence. In this work we seek to determine how intelligent these classes of control systems are by quantifying their level of adaptability and learning. First we describe the stages of development towards intelligent control and present a definition based on literature. Based on the key elements of this definition, we propose a novel taxonomy of intelligent control methods, which assesses the extent to which they handle uncertainties in three areas: the environment, the controller, and the goals. This taxonomy is applicable to a variety of robotic and other autonomous systems, which we demonstrate through several examples of intelligent control methods and their classifications. Looking at the spread of classifications based on this taxonomy can help researchers identify where control systems can be made more intelligent.
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23

Sawaragi, Tetsuo. "Emerging Intelligence for Next-Generation Intelligent Systems and Control." Journal of Robotics and Mechatronics 12, no. 6 (December 20, 2000): 614–27. http://dx.doi.org/10.20965/jrm.2000.p0614.

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This paper presents a general survey on ""intelligence"" from broad perspectives of living organisms including insects, animals, and humans. After current paradigm shifts commonly occurring in interdisciplinary academic areas are reviewed, we show that the commonly focused interest therein is reconsideration about mutual and inseparable relationships between the external environment and the internal of the agent, that is an actor, an observer, a cognizer, and an interpreter. We emphasize the fact that autonomous systems can be characterized by their self-organizing capabilities driven mainly by internal coherence produced by internal mutual relations among components, rather than described by inputs from an external environment. Then, we introduce the subject of semiotics, a new interdisciplinary branch of science, and its potential contribution to bridging between biological intelligence and machine intelligence. We also describe its relationships with current hot topics of embodiment and symbol grounding often discussed by robotics researchers. Finally, we present our ongoing series of work related to the above topics.
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24

Ye, Likai. "Traffic Light Control System." Journal of Theory and Practice of Engineering Science 4, no. 03 (March 19, 2024): 111–24. http://dx.doi.org/10.53469/jtpes.2024.04(03).11.

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The traffic light control system is a key component of urban traffic management, and its performance directly impacts the smooth flow of traffic and traffic safety. Based on some issues existing in the current traffic signal control systems, this paper proposes a novel traffic light control system based on intelligent algorithms. This system combines artificial intelligence technology with traditional traffic signal control methods to achieve intelligent control of traffic signals, thereby enhancing traffic efficiency and road safety. The paper provides a detailed explanation of the system's design principles, algorithm implementation, and experimental results. Comparative experiments demonstrate that the system has significant advantages in reducing traffic congestion and improving vehicle passage efficiency. Finally, the paper discusses possible future improvement directions and application scenarios, envisioning the development prospects of traffic light control systems in the future.
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25

Cabrera, Juan A. "Advances in Intelligent Vehicle Control." Sensors 22, no. 22 (November 9, 2022): 8622. http://dx.doi.org/10.3390/s22228622.

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Advanced intelligent vehicle control systems have evolved in the last few decades thanks to the use of artificial-intelligence-based techniques, the appearance of new sensors, and the development of technology necessary for their implementation [...]
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26

Zheng, Zheng Xing, Guo Min Tang, and Li Min Liu. "A SoC for Intelligent Control Systems." Advanced Materials Research 571 (September 2012): 514–17. http://dx.doi.org/10.4028/www.scientific.net/amr.571.514.

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Intelligent control is a new direction of industrial automation. An intelligent control system is composed of algorithm, software and hardware. SoC is one of the best embedded system hardware. SoC may get some new progress for intelligent control. In this paper, intelligent control, SoC and some intelligent controller based on SoC are discussed. The new controller can be the smaller and more reliable.
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27

CHANG, SHI-KUO. "EDITORIAL: A GENERAL FRAMEWORK FOR SLOW INTELLIGENCE SYSTEMS." International Journal of Software Engineering and Knowledge Engineering 20, no. 01 (February 2010): 1–15. http://dx.doi.org/10.1142/s0218194010004578.

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Contrary to popular belief, not all intelligent systems have Quick Intelligence. There are a surprisingly large number of intelligent systems, quasi-intelligent systems and semi-intelligent systems that have Slow Intelligence. Such Slow Intelligence Systems are often neglected in mainstream research on intelligent systems, but they are really worthy of our attention and emulation. I will describe the characteristics of Slow Intelligence Systems and present a general framework for Slow Intelligence Systems. I will then discuss evolutionary query processing and mission control in emergency management systems as two examples of Artificial Slow Intelligence Systems. Researchers and practitioners are both invited to explore the applications of Slow Intelligence Systems in software engineering and knowledge engineering, and publish their findings in IJSEKE.
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Kaymakci, Can, Raoul Laribi, and Alexander Sauer. "Intelligente Regelung für Stromspeichersysteme/Intelligent control for energy storage systems." wt Werkstattstechnik online 111, no. 03 (2021): 167–71. http://dx.doi.org/10.37544/1436-4980-2021-03-75.

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In zahlreichen mechatronischen Anwendungen kann der Einsatz von Stromspeichern die notwendige Anschlussleistung reduzieren und die Energieeffizienz durch die Nutzung von Bremsenergie erhöhen. Ein Stromspeichersystem in eine Fremdmaschine zu integrieren stellt eine Herausforderung bei der Inbetriebnahme von Hardware und Software dar. Eine automatische Systemidentifikation und Lastprognose anhand von Messdaten erleichtert den Regelungsentwurf für das mechatronische System. Dieser Beitrag erläutert eine Vorgehensmethodik für die Vorauswahl von geeigneten Methoden für die Modellbildung zur Lastprognose.   In numerous mechatronic applications it is possible to apply electricity storage devices for reducing the necessary installed load and increasing energy efficiency and thus to make use of the braking energy. The integration of an electricity storage system into a third-party machine is challenging with regard to the commissioning of hardware and software. An intelligent control system facilitates system identification based on measurement data and load forecasting within the mechatronic system. This article explains a procedure for pre-selecting suitable methods for load forecasting models.
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Rudas, Imre J. "Intelligent Engineering Systems." Journal of Advanced Computational Intelligence and Intelligent Informatics 2, no. 3 (June 20, 1998): 69–71. http://dx.doi.org/10.20965/jaciii.1998.p0069.

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Building intelligent systems has been one of the great challenges since the early days of human culture. From the second half of the 18th century, two revolutionary changes played the key role in technical development, hence in creating engineering and intelligent engineering systems. The industrial revolution was made possible through technical advances, and muscle power was replaced by machine power. The information revolution of our time, in turn, canbe characterized as the replacement of brain power by machine intelligence. The technique used to build engineering systems and replace muscle power can be termed "Hard Automation"1) and deals with industrial processes that are fixed and repetitive in nature. In hard automation, the system configuration and the operations are fixed and cannot be changed without considerable down-time and cost. It can be used, however, particularly in applications calling for fast, accurate operation, when manufacturing large batches of the same product. The "intelligent" area of automation is "Soft Automation," which involves the flexible, intelligent operation of an automated process. In flexible automation, the task is programmable and a work cell must be reconfigured quickly to accommodate a product change. It is particularly suitable for plant environments in which a variety of products is manufactured in small batches. Processes in flexible automation may have unexpected or previously unknown conditions, and would require a certain degree of "machine" intelligence to handle them.The term machine intelligence has been changing with time and is machinespecific, so intelligence in this context still remains more or less a mysterious phenomenon. Following Prof. Lotfi A. Zadeh,2) we consider a system intelligent if it has a high machine intelligence quotient (MIQ). As Prof. Zadeh stated, "MIQ is a measure of intelligence of man-made systems," and can be characterized by its well defined dimensions, such as planning, decision making, problem solving, learning reasoning, natural language understanding, speech recognition, handwriting recognition, pattern recognition, diagnostics, and execution of high level instructions.Engineering practice often involves complex systems having multiple variable and multiple parameter models, sometimes with nonlinear coupling. The conventional approaches for understanding and predicting the behavior of such systems based on analytical techniques can prove to be inadequate, even at the initial stages of setting up an appropriate mathematical model. The computational environment used in such an analytical approach is sometimes too categoric and inflexible in order to cope with the intricacy and complexity of real-world industrial systems. It turns out that, in dealing with such systems, one must face a high degree of uncertainty and tolerate great imprecision. Trying to increase precision can be very costly.In the face of the difficulties above, Prof. Zadeh proposes a different approach for Machine Intelligence. He separates Hard Computing techniques based Artificial Intelligence from Soft Computing techniques based Computational Intelligence.•Hard computing is oriented toward the analysis and design of physical processes and systems, and is characterized by precision, formality, and categorization. It is based on binary logic, crisp systems, numerical analysis, probability theory, differential equations, functional analysis, mathematical programming approximation theory, and crisp software.•Soft computing is oriented toward the analysis and design of intelligent systems. It is based on fuzzy logic, artificial neural networks, and probabilistic reasoning, including genetic algorithms, chaos theory, and parts of machine learning, and is characterized by approximation and dispositionality.In hard computing, imprecision and uncertainty are undesirable properties. In soft computing, the tolerance for imprecision and uncertainty is exploited to achieve an acceptable solution at low cost, tractability, and a high MIQ. Prof. Zadeh argues that soft rather than hard computing should be viewed as the foundation of real machine intelligence. A center has been established - the Berkeley Initiative for Soft Computing (BISC) - and he directs it at the University of California, Berkeley. BISC devotes its activities to this concept.3) Soft computing, as he explains2),•is a consortium of methodologies providing a foundation for the conception and design of intelligent systems,•is aimed at formalizing of the remarkable human ability to make rational decision in an uncertain, imprecise environment.The guiding principle of soft computing, given by Prof. Zadeh2) is: Exploit the tolerance for imprecision, uncertainty, and partial truth to achieve tractability, robustness, low solution cost, and better rapport with reality.Fuzzy logic is mainly concerned with imprecision and approximate reasoning, neurocomputing mainly with learning and curve fitting, genetic computation mainly with searching and optimization and probabilistic reasoning mainly with uncertainty and propagation of belief. The constituents of soft computing are complementary rather than competitive. Experience gained over the past decade indicates that it can be more effective to use them combined, rather than exclusively.Based on this approach, machine intelligence, including artificial intelligence and computational intelligence (soft computing techniques) is one pillar of Intelligent Engineering Systems. Hundreds of new results in this area are published in journals and international conference proceedings. One such conference, organized in Budapest, Hungary, on September 15-17, 1997, was titled'IEEE International Conference on Intelligent Engineering Systems 1997' (INES'97), sponsored by the IEEE Industrial Electronics Society, IEEE Hungary Section, Bá{a}nki Doná{a}t Polytechnic, Hungary, National Committee for Technological Development, Hungary, and in technical cooperation with the IEEE Robotics & Automation Society. It had around 100 participants from 29 countries. This special issue features papers selected from those papers presented during the conference. It should be pointed out that these papers are revised and expanded versions of those presented.The first paper discusses an intelligent control system of an automated guided vehicle used in container terminals. Container terminals, as the center of cargo transportation, play a key role in everyday cargo handling. Learning control has been applied to maintaining the vehicle's course and enabling it to stop at a designatedlocation. Speed control uses conventional control. System performance system was evaluated by simulation, and performance tests slated for a test vehicle.The second paper presents a real-time camera-based system designed for gaze tracking focused on human-computer communication. The objective was to equip computer systems with a tool that provides visual information about the user. The system detects the user's presence, then locates and tracks the face, nose and both eyes. Detection is enabled by combining image processing techniques and pattern recognition.The third paper discusses the application of soft computing techniques to solve modeling and control problems in system engineering. After the design of classical PID and fuzzy PID controllers for nonlinear systems with an approximately known dynamic model, the neural control of a SCARA robot is considered. Fuzzy control is discussed for a special class of MIMO nonlinear systems and the method of Wang generalized for such systems.The next paper describes fuzzy and neural network algorithms for word frequency prediction in document filtering. The two techniques presented are compared and an alternative neural network algoritm discussed.The fifth paper highlights the theory of common-sense knowledge in representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning, and experimental results using this method presented.The next paper introduces an expert consulting system that employs software agents to manage distributed knowledge sources. These individual software agents solve users' problems either by themselves or thorough mutual cooperation.The last paper presents a methodology for creating and applying a generic manufacturing process model for mechanical parts. Based on the product model and other up-to-date approaches, the proposed model involves all possible manufacturing process variants for a cluster of manufacturing tasks. The application involves a four-level model structure and Petri net representation of manufacturing process entities. Creation and evaluation of model entities and representation of the knowledge built in the shape and manufacturing process models are emphasised. The proposed process model is applied in manufacturing process planning and production scheduling.References:1) C. W. De Silva, "Automation Intelligence," Engineering Application of Artificial Intelligence, 7-5, 471-477, (1994).2) L. A. Zadeh, "Fuzzy Logic, Neural Networks and Soft Computing," NATO Advanced Studies Institute on Soft Computing and Its Application, Antalya, Turkey, (1996).3) L. A. Zadeh, "Berkeley Initiative_in Soft Computing," IEEE Industrial Electronics Society Newsletter. 41-3, 8-10, (1994).
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30

Kawaji, Shigeyasu. "A Control-Theoretic View of Intelligent Control." Journal of Robotics and Mechatronics 12, no. 6 (December 20, 2000): 605–13. http://dx.doi.org/10.20965/jrm.2000.p0605.

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The term ""intelligent"" heralds new developments in many established fields with traditional assumptions in research and development. In the case of intelligent control, an attempt was first made to utilize methodologies such as model-based control theory, artificial intelligence and operations research, and recently to draw inspiration from nature, biology, and artificial life. Common methodology in both eras is promoted that is more accepting of heuristics and approximations than is the case with most research in control engineering. However, the methodology is less insistent on theoretical rigor and completeness. There is an inherent difficulty in succinct characterization of intelligent control. Many people have a common intellectual understanding of intelligent control, but some significant questions about common understanding have been yet to be unsolved such what is as the definition of intelligent control, and why intelligent control systems are generally structured in a hierarchical way. These questions should be solved before entering the second stage of an intelligent control framework. In this paper, we present the controltheoretic view of intelligent control technology. First, the need of a new approach is introduced based on the concept of control, and secondly typical intelligent control techniques are provided with example of inverted pendulum. Finally the definition and fundamental architecture of intelligent control are discussed and an example from a biped locomotion robot helps clarify the effectiveness of hierarchical architectures.
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31

Xu-Yen, Tu. "Intelligent control and intelligent management for large scale systems." IFAC Proceedings Volumes 19, no. 17 (March 1986): 87–91. http://dx.doi.org/10.1016/s1474-6670(17)69391-1.

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32

Teige, Christian, Alexander Fertig, Berend Denkena, Benjamin Bergmann, and Matthias Weigold. "Intelligente Vernetzung für die Fräsbearbeitung/Intelligent networking for milling processes." wt Werkstattstechnik online 111, no. 01-02 (2021): 14–19. http://dx.doi.org/10.37544/1436-4980-2021-01-02-18.

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Im Forschungsprojekt „TensorMill“ wird die Umsetzung einer intelligent vernetzten autonomen Fertigung von sicherheitsrelevanten Bauteilen in der Luftfahrtindustrie angestrebt. Dabei soll mithilfe von Künstlicher Intelligenz (KI) auf möglichst viele Situationen im Fertigungsprozess reagiert werden. Dies dient dem Ziel, die Produktivität und Prozesssicherheit bei der Herstellung der sicherheitsrelevanten Integralbauteile zu erhöhen.   The „TensorMill“ research project aims to implement an intelligently networked autonomous production of safety-relevant components in the aviation industry. The aim is to use artificial intelligence (AI) for reacting to as many situations in the manufacturing process as possible. This serves the goal of increasing productivity and process reliability in the production of safety-relevant integral components.
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33

Ito, Koji. "Intelligent Motion Control. III. Future Technologies of Intelligent Motion Control-Emergent Systems." IEEJ Transactions on Industry Applications 116, no. 8 (1996): 816–19. http://dx.doi.org/10.1541/ieejias.116.816.

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34

Vladareanu, Luige. "Advanced Intelligent Control through Versatile Intelligent Portable Platforms." Sensors 20, no. 13 (June 29, 2020): 3644. http://dx.doi.org/10.3390/s20133644.

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Deep research and communicating new trends in the design, control and applications of the real time control of intelligent sensors systems using advanced intelligent control methods and techniques is the main purpose of this research. The innovative multi-sensor fusion techniques, integrated through the Versatile Intelligent Portable (VIP) platforms are developed, combined with computer vision, virtual and augmented reality (VR&AR) and intelligent communication, including remote control, adaptive sensor networks, human-robot (H2R) interaction systems and machine-to-machine (M2M) interfaces. Intelligent decision support systems (IDSS), including remote sensing, and their integration with DSS, GA-based DSS, fuzzy sets DSS, rough sets-based DSS, intelligent agent-assisted DSS, process mining integration into decision support, adaptive DSS, computer vision based DSS, sensory and robotic DSS, are highlighted in the field of advanced intelligent control.
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35

Zilberova, I., K. Petrov, and K. H. Lamy. "Innovation Intelligent Control Systems in Construction." IOP Conference Series: Materials Science and Engineering 1079, no. 3 (March 1, 2021): 032087. http://dx.doi.org/10.1088/1757-899x/1079/3/032087.

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36

I.Yu. MAKUSHEV. "Making Intelligent Aircraft Onboard Control Systems." Military Thought 29, no. 001 (March 31, 2020): 159–68. http://dx.doi.org/10.21557/mth.59216006.

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37

Tian, Hua. "Examples Analysis of Intelligent Control Systems." IFAC Proceedings Volumes 31, no. 20 (July 1998): 991–96. http://dx.doi.org/10.1016/s1474-6670(17)41927-6.

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38

Sanz, Ricardo, Angel de Antonio, Miguel Segarra, Fernando Matía, Agustín Jiménez, and Ramón Galán. "Design patterns for intelligent control systems." IFAC Proceedings Volumes 32, no. 2 (July 1999): 8728–33. http://dx.doi.org/10.1016/s1474-6670(17)57489-3.

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39

Tian, Hua. "Structural modeling of intelligent control systems." IFAC Proceedings Volumes 32, no. 2 (July 1999): 5985–90. http://dx.doi.org/10.1016/s1474-6670(17)57021-4.

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40

Newell, Allen, and David Steier. "Intelligent control of external software systems." Artificial Intelligence in Engineering 8, no. 1 (January 1993): 3–21. http://dx.doi.org/10.1016/0954-1810(93)90027-d.

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41

Ravindranathan, Mohan, and Roy Leitch. "Model switching in intelligent control systems." Artificial Intelligence in Engineering 13, no. 2 (April 1999): 175–87. http://dx.doi.org/10.1016/s0954-1810(98)00016-8.

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42

Abd-El-Raheem, Gamal, Abd-El-fattah Mahmoud, Amer Abd-El-fattah, and Hosam Ismail Hasan. "INTELLIGENT CONTROL OF MAGNETIC SUSPENSION SYSTEMS." JES. Journal of Engineering Sciences 34, no. 3 (May 1, 2006): 875–901. http://dx.doi.org/10.21608/jesaun.2006.110570.

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43

Buss, M., and H. Hashimoto. "Intelligent control for human-machine systems." IEEE/ASME Transactions on Mechatronics 1, no. 1 (March 1996): 50–55. http://dx.doi.org/10.1109/3516.491409.

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44

Lin, Jeen, and Ruey-Jing Lian. "Intelligent Control of Active Suspension Systems." IEEE Transactions on Industrial Electronics 58, no. 2 (February 2011): 618–28. http://dx.doi.org/10.1109/tie.2010.2046581.

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45

Burnham, K. J., O. C. L. Haas, and D. J. G. James. "Intelligent systems for optimisation and control." Kybernetes 29, no. 5/6 (July 2000): 716–33. http://dx.doi.org/10.1108/03684920010333152.

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Balasubramanian, S., X. Zhang, and D. H. Norrie. "Intelligent control for holonic manufacturing systems." Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 214, no. 10 (October 2000): 953–61. http://dx.doi.org/10.1243/0954405001518026.

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47

Davis, Wayne, Albert Jones, and Abdol Saleh. "Generic architecture for intelligent control systems." Computer Integrated Manufacturing Systems 5, no. 2 (May 1992): 105–13. http://dx.doi.org/10.1016/0951-5240(92)90005-w.

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48

Petrov, A. M., A. N. Popov, and O. N. Kuzyakov. "Improvement of intelligent control systems architecture." Automation and informatization of the fuel and energy complex, no. 4 (2023): 15–22. http://dx.doi.org/10.33285/2782-604x-2023-4(597)-15-22.

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49

Khaleel, Mohamed. "Intelligent Control Techniques for Microgrid Systems." Brilliance: Research of Artificial Intelligence 3, no. 1 (March 23, 2023): 56–67. http://dx.doi.org/10.47709/brilliance.v3i1.2192.

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Microgrids (MG) are complex systems that integrate distributed energy resources to provide reliable and efficient power to local loads. Due to the dynamic and uncertain nature of the MG environment, intelligent control techniques have become a popular solution to ensure optimal performance. This paper provides an overview of the recent advances in intelligent control techniques applied in MG, including neural networks, model predictive control, game theory, deep reinforcement learning, and Bayesian controllers. The paper also presents a discussion of the advantages and limitations of these techniques, highlighting the challenges associated with implementing them in MG systems. Finally, investigation of the existing literature on the performance of intelligent control techniques in MG systems is presented, providing insights into their effectiveness in improving the energy efficiency, stability, and reliability of MG systems.
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Koutb, Magdi A., Nabila M. El-Rabaie, Hamdi A. Awad, and Ibrahim A. Abd El-Hamid. "Environmental control for plants using intelligent control systems." IFAC Proceedings Volumes 37, no. 2 (March 2004): 101–6. http://dx.doi.org/10.1016/s1474-6670(17)38698-6.

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