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Journal articles on the topic 'Intelligent Adaptive Control'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Rheem, JaeYeol. "Intelligent Adaptive Active Noise Control in Non-stationary Noise Environments." JOURNAL OF THE ACOUSTICAL SOCIETY OF KOREA 32, no. 5 (2013): 408. http://dx.doi.org/10.7776/ask.2013.32.5.408.

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2

Posada, J., and M. Sanjuan. "INTELLIGENT ADAPTIVE DYNAMIC MATRIX CONTROL." IFAC Proceedings Volumes 41, no. 2 (2008): 11269–74. http://dx.doi.org/10.3182/20080706-5-kr-1001.01909.

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3

Dumont, Guy A. "Adaptive control and intelligent automation." Chemometrics and Intelligent Laboratory Systems 8, no. 3 (1990): 275–79. http://dx.doi.org/10.1016/0169-7439(90)80011-t.

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4

Yamazaki, Yoshitake. "Adaptive-intelligent control of intelligence flows as microheat transfer." Computers & Electrical Engineering 29, no. 1 (2003): 85–100. http://dx.doi.org/10.1016/s0045-7906(01)00012-x.

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5

Liu, Yanming, Li Zuo, and Chaojun Wang. "Intelligent adaptive control in milling processes." International Journal of Computer Integrated Manufacturing 12, no. 5 (1999): 453–60. http://dx.doi.org/10.1080/095119299130182.

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6

Prakash, Raghupathy, and Rajapalan Anita. "Robust Model Reference Adaptive Intelligent Control." International Journal of Control, Automation and Systems 10, no. 2 (2012): 396–406. http://dx.doi.org/10.1007/s12555-012-0219-6.

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7

Vladareanu, Luige. "Advanced Intelligent Control through Versatile Intelligent Portable Platforms." Sensors 20, no. 13 (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

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8

Zhang, Hao, Ya Jie Zhang, and Yan Gu Zhang. "Intelligent Self-Adaptive Control Method Based on RBFNN and its Application in Hydraulic Control." Advanced Materials Research 1030-1032 (September 2014): 1488–92. http://dx.doi.org/10.4028/www.scientific.net/amr.1030-1032.1488.

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We proposed an intelligent self-adaptive control method based on RBFNN in this paper, dynamic identification model of nonlinear control system is built based on radial basis neural network, mixed intelligent method of dynamic self-adaptive internal model control is developed by adjusting online for nonlinear control system. We applied the intelligent self-adaptive control method to nonlinear hydraulic control, simulation shows the dynamic characteristic is greatly improved by the intelligent control strategy for nonlinear control system, good tracking and control effect is reached in condition

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9

Zhou, Jun. "Intelligent Information Control for Air System." Applied Mechanics and Materials 310 (February 2013): 502–5. http://dx.doi.org/10.4028/www.scientific.net/amm.310.502.

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PID control has been widely applied in the industrial process control because of its robust and easy realization, but it is difficult to tune the parameters of PID controller, which often leads to oscillation and overshoot. Due to no repetition and random of adaptive fuzzy PID control, the authors propose a method to search for normalization PID controller parameters based on adaptive fuzzy PID control, which can be expected to have higher ability of searching for global optimal PID parameters according to the performance index of control system . The MATLAB simulation of the Adaptive fuzzy PI

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10

Kabaldin, Y. G., D. A. Shatagin, M. S. Anosov, and A. M. Kuzmishina. "Intelligent Control of Technological Systems in Digital Manufacturing." Proceedings of Higher Educational Institutions. Маchine Building, no. 01 (718) (January 2020): 3–12. http://dx.doi.org/10.18698/0536-1044-2020-1-3-12.

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This paper presents an analysis of the development of adaptive control systems for CNC machines. It is shown that the construction of systems for optimal control of machining processes is based on such approaches as artificial intelligence, genetic algorithms for optimizing processing modes, expert systems for knowledge accumulation, cloud technologies and the development of digital twins of the equipment. An adaptive system of intelligent control of a CNC machine is developed based on training of a neural network model, which can improve the quality of machining parts and reduce the wear of t

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11

Wang, Ze Fang, and Chen Liu. "Study on Greenhouse System Modeling Based on Adaptive Fuzzy Predictive Control." Applied Mechanics and Materials 602-605 (August 2014): 1237–39. http://dx.doi.org/10.4028/www.scientific.net/amm.602-605.1237.

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In recent years, these governments are committed to intelligent greenhouse research, intelligent greenhouse system is a kind of resource conservation effective agricultural development technology, it is in the common greenhouse basis, combined with modern computer automatic control technology, intelligent sensing technology, artificial intelligence and expert system in high-tech field to develop, provide seasonal irrelevant for crop growth environment in a computer integrated control, to realize the various crops industrial production of high quality、 high efficient and low consumption[1]. Wit

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12

Liu, Ze Min. "Study on Intelligent Self-Adaptive Control System." Applied Mechanics and Materials 539 (July 2014): 620–24. http://dx.doi.org/10.4028/www.scientific.net/amm.539.620.

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With the rapid development of China's industry, the use of the control system has become more and more extensive. However, with the complicating of the production system, the traditional control system has been unable to meet the needs of the current industry. Effectively bring the genetic algorithm of the neural network into the control system can solve this problem. Here, it firstly describes the neural network, genetic algorithm principle, operation procedures and the characteristics; secondly, analyzes the principle and lack of conventional PID controller; finally, effectively combines gen

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13

Wang, Wen Jie, and Bing Yong Tang. "A fuzzy adaptive method for intelligent control." Expert Systems with Applications 16, no. 1 (1999): 43–48. http://dx.doi.org/10.1016/s0957-4174(98)00027-x.

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14

Hilhorst, R. A., J. van Amerongen, P. Löhnberg, and H. J. A. F. Tulleken. "Intelligent Adaptive Control of Mode-Switch Processes." IFAC Proceedings Volumes 24, no. 1 (1991): 145–50. http://dx.doi.org/10.1016/s1474-6670(17)51311-7.

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15

Yamamoto, S. "Industrial Developments in Intelligent and Adaptive Control." IFAC Proceedings Volumes 24, no. 1 (1991): 171–78. http://dx.doi.org/10.1016/s1474-6670(17)51315-4.

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16

Pait, Felipe M. "Functional adaptive control—an intelligent systems approach." Automatica 38, no. 11 (2002): 2029–30. http://dx.doi.org/10.1016/s0005-1098(02)00097-3.

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17

Juuso, Esko K. "Modelling and Simulation in Adaptive Intelligent Control." SNE Simulation Notes Europe 26, no. 2 (2016): 109–16. http://dx.doi.org/10.11128/sne.26.on.10338.

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18

Nazaruddin, Yul Y., and M. Aria. "ADAPTIVE-PREDICTIVE CONTROL WITH INTELLIGENT VIRTUAL SENSOR." IFAC Proceedings Volumes 38, no. 1 (2005): 263–68. http://dx.doi.org/10.3182/20050703-6-cz-1902.01122.

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19

Fabri,, SG, V. Kadirkamanathan,, and PJ Eagle,. "Functional Adaptive Control: An Intelligent Systems Approach." Applied Mechanics Reviews 55, no. 6 (2002): B108—B109. http://dx.doi.org/10.1115/1.1508146.

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20

Kulkarni, N. V., and K. KrishnaKumar. "Intelligent engine control using an adaptive critic." IEEE Transactions on Control Systems Technology 11, no. 2 (2003): 164–73. http://dx.doi.org/10.1109/tcst.2003.809254.

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21

Dideková, Zuzana, Štefan Kozák, Slavomír Kajan, and Alena Kozáková. "Intelligent Hybrid Control Based on Adaptive Switching." IFAC-PapersOnLine 50, no. 1 (2017): 11221–26. http://dx.doi.org/10.1016/j.ifacol.2017.08.2107.

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22

Yamazaki, Yoshitake, Geuntaek Kang, and Moyuru Ochiai. "Adaptive-intelligent control by neural-net systems." International Journal of Intelligent Systems 13, no. 6 (1998): 503–18. http://dx.doi.org/10.1002/(sici)1098-111x(199806)13:6<503::aid-int6>3.0.co;2-p.

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23

Timofeyev, A. V., and R. M. Yussupov. "Evolution of intelligent control in adaptive systems." International Journal of Adaptive Control and Signal Processing 6, no. 3 (1992): 193–200. http://dx.doi.org/10.1002/acs.4480060307.

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24

Timofeev, A. V. "Adaptive-intelligent control of robots and technological equipment of intelligent manufacturing." Journal of Intelligent Manufacturing 2, no. 4 (1991): 245–48. http://dx.doi.org/10.1007/bf01471111.

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25

Li, Zheng Qiang, and Jing Zhou Zhao. "Method and Application of Intelligent Reconfigurable Control." Applied Mechanics and Materials 236-237 (November 2012): 289–96. http://dx.doi.org/10.4028/www.scientific.net/amm.236-237.289.

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In this paper we propose an intelligent adaptive retrofit reconfigurable controller in which the full system states are reliably measurable and available for feedback and diagnosis. The proposed approach retains the existing nominal controller and adds a suitably chosen signal that assures that the desired closed-loop performance is achieved despite the failure. The approach is based on the properties of the system controlled by the nominal controller, and judiciously chosen adaptation signals. Furthermore, some representative methods of intelligent reconfigurable control are introduced, espec

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26

Zhang, Bi, Zhizhong Mao, and Tingfeng Zhang. "Intelligent control for Hammerstein nonlinear systems with arbitrary deadzone input." Transactions of the Institute of Measurement and Control 39, no. 4 (2015): 567–78. http://dx.doi.org/10.1177/0142331215611934.

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In this paper, a new intelligent control scheme based on multiple models and neural networks is proposed to adaptively control a class of Hammerstein nonlinear systems with arbitrary deadzone input. This approach consists of a linear robust adaptive controller, multiple neural networks-based nonlinear adaptive controllers and a switching mechanism. Since the control input is derived from a modified certainty equivalent principle, the manner in which the closed-loop stability is established forms the main contribution. To show the usefulness of the developed results, three simulation examples,

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27

Faudzi, Ahmad'Athif Mohd, Koichi Suzumori, Shuichi Wakimoto, and Hiroyuki Kudo. "1A1-B16 Control Approaches and Detecting Function of Intelligent Cylinder for Human Adaptive Mechanism." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2008 (2008): _1A1—B16_1—_1A1—B16_4. http://dx.doi.org/10.1299/jsmermd.2008._1a1-b16_1.

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28

Ai, Yan Jin, and Tie Cheng Gao. "An Intelligent Control System for LED Headlamp." Advanced Materials Research 889-890 (February 2014): 982–86. http://dx.doi.org/10.4028/www.scientific.net/amr.889-890.982.

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An intelligent control system for LED headlamp was developed. This system features include two features : LED lamp system of self-adaptive control and use of LED lighting realize visible optical communication. The realization of the control system of intelligent lights include: LED headlights Lord adaptive dimmer, high power LED lamp overheating protection, light and automatic operation, front fog lamps and intelligent control of the lights. Use LED lamp as light lighting equipment, a traditional light it is difficult to have the power to control and adaptive control mode. Based on the optical

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29

Li, Meng, Yong Chen, Anjian Zhou, Wen He, and Xu Li. "Adaptive tracking control for networked control systems of intelligent vehicle." Information Sciences 503 (November 2019): 493–507. http://dx.doi.org/10.1016/j.ins.2019.07.003.

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30

MUROTSU, Yoshisada, Hiroshi OKUBO, Kei SENDA, and Kazuhiro HISAJI. "Control of Intelligent Adaptive Structures. 1st Report: Optimal Trajectory Control." Transactions of the Japan Society of Mechanical Engineers Series C 57, no. 543 (1991): 3518–25. http://dx.doi.org/10.1299/kikaic.57.3518.

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31

Han, Peng, Xiu Sheng Cheng, Yong Dao Song, Yin Shu Wang, and Xi Liu. "Study on the Intelligent Control Technology for Wet Dual Clutch Transmission." Advanced Materials Research 712-715 (June 2013): 2179–82. http://dx.doi.org/10.4028/www.scientific.net/amr.712-715.2179.

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An intelligent control system of wet dual clutch transmission was presented in this paper,and an intelligent control arithmetic based on neural adaptive PSD was established to realize precise control of clutch pressure.The transfer characteristics of clutch torque were analyzed,and the clutch torque model was established Due to the characteristic of the clutch control valve,the clutch pressure was precisely controlled in the neural adaptive PSD controller. The shift tests showed that the intelligent control arithmetic based on neural adaptive PSD had good adaptive ability,which could meet the

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32

YAMADA, Yuuji. "Intelligent Adaptive Control for a Pneumatic Servo System." TRANSACTIONS OF THE JAPAN FLUID POWER SYSTEM SOCIETY 34, no. 1 (2003): 1–7. http://dx.doi.org/10.5739/jfps.34.1.

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33

van Amerongen, J., R. A. Hilhorst, P. Löhnberg, and H. J. A. F. Tulleken. "An Intelligent Supervisor for Adaptive Mode-Switch Control." IFAC Proceedings Volumes 26, no. 2 (1993): 839–42. http://dx.doi.org/10.1016/s1474-6670(17)48391-1.

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34

Tokhi, M. O., and S. M. Veres. "Intelligent adaptive active control of noise and vibration." IFAC Proceedings Volumes 32, no. 2 (1999): 8740–45. http://dx.doi.org/10.1016/s1474-6670(17)57491-1.

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35

Zhang, Shuo, Xuan Zhao, Guohua Zhu, Peilong Shi, Yue Hao, and Lingchen Kong. "Adaptive trajectory tracking control strategy of intelligent vehicle." International Journal of Distributed Sensor Networks 16, no. 5 (2020): 155014772091698. http://dx.doi.org/10.1177/1550147720916988.

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The trajectory tracking control strategy for intelligent vehicle is proposed in this article. Considering the parameters perturbations and external disturbances of the vehicle system, based on the vehicle dynamics and the preview follower theory, the lateral preview deviation dynamics model of the vehicle system is established which uses lateral preview position deviation, lateral preview velocity deviation, lateral preview attitude angle deviation, and lateral preview attitude angle velocity deviation as the tracking state variables. For this uncertain system, the adaptive sliding mode contro

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36

Wilson, Eric L., Charles L. Karr, and James P. Bennett. "An Adaptive, Intelligent Control System for Slag Foaming." Applied Intelligence 20, no. 2 (2004): 165–77. http://dx.doi.org/10.1023/b:apin.0000013338.39348.46.

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37

Lavu, Brijesh C., Marco P. Schoen, and Ajay Mahajan. "Adaptive intelligent control of ionic polymer–metal composites." Smart Materials and Structures 14, no. 4 (2005): 466–74. http://dx.doi.org/10.1088/0964-1726/14/4/002.

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38

Khayyam, Hamid. "Adaptive intelligent control of vehicle air conditioning system." Applied Thermal Engineering 51, no. 1-2 (2013): 1154–61. http://dx.doi.org/10.1016/j.applthermaleng.2012.10.028.

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39

Chen, Chiu-Hsiung, Chih-Min Lin, and Te-Yu Chen. "Intelligent adaptive control for MIMO uncertain nonlinear systems." Expert Systems with Applications 35, no. 3 (2008): 865–77. http://dx.doi.org/10.1016/j.eswa.2007.07.053.

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40

Mehrabian, Ali Reza, Caro Lucas, and Jafar Roshanian. "Aerospace launch vehicle control: an intelligent adaptive approach." Aerospace Science and Technology 10, no. 2 (2006): 149–55. http://dx.doi.org/10.1016/j.ast.2005.11.002.

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41

SUZUMORI, Koichi, Takefumi KANDA, Junichi TANAKA, and Yukihiro MIYASHITA. "Development of intelligent cylinder : 2nd report; Adaptive control using intelligent pneumatic cylinder." Proceedings of the JSME annual meeting 2004.4 (2004): 263–64. http://dx.doi.org/10.1299/jsmemecjo.2004.4.0_263.

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42

HASSAN, M., A. SADEK, M. H. ATTIA, and V. THOMSON. "INTELLIGENT MACHINING: REAL-TIME TOOL CONDITION MONITORING AND INTELLIGENT ADAPTIVE CONTROL SYSTEMS." Journal of Machine Engineering Vol.18, No.1 (2018): 5–18. http://dx.doi.org/10.5604/01.3001.0010.8811.

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Unmanned manufacturing systems has recently gained great interest due to the ever increasing requirements of optimized machining for the realization of the fourth industrial revolution in manufacturing ‘Industry 4.0’. Real-time tool condition monitoring (TCM) and adaptive control (AC) machining system are essential technologies to achieve the required industrial competitive advantage, in terms of reducing cost, increasing productivity, improving quality, and preventing damage to the machined part. New AC systems aim at controlling the process parameters, based on estimating the effects of the

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43

ZHANG, YUMING, QIYUE WANG, and YUKANG LIU. "Adaptive Intelligent Welding Manufacturing." Welding Journal 100, no. 01 (2021): 63–83. http://dx.doi.org/10.29391/2021.100.006.

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Optimal design of the welding procedure gives the desired welding results under nominal welding conditions. During manufacturing, where the actual welding manufacturing conditions often deviate from the nominal ones used in the design, applying the designed procedure will produce welding results that are different from the desired ones. Adaption is needed to make corrections and adjust some of the welding parameters from those specified in the design. This is adaptive welding. While human welders can be adaptive to make corrections and adjustments, their performance is limited by their physica

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44

Jia, De Li, Jin Song He, Ji Chao Ning, and Chun Sheng Wang. "Research on Intelligent Control Strategy of Inverted Plasma Cutting Power." Advanced Materials Research 179-180 (January 2011): 1223–28. http://dx.doi.org/10.4028/www.scientific.net/amr.179-180.1223.

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The inverted plasma cutting power supply has multi-variable, nonlinear, strong coupling and time-varying characteristics and technological requirements. This paper proposes a decoupling control strategy based on fuzzy neural network expert system for the multi-parameter dynamic coupling and the uncertainty of the optimal output of the cutting process. It achieves the reasoning and decision-making through the fuzzy production rules. It adjusts the parameters of the neural network by adaptive algorithm, thus gets the optimal reference current of closed-loop controller, and then achieves the give

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45

Shorikov, Andrey F., and Elena V. Butsenko. "Intelligent software system for optimizing adaptive control of business planning processes." Journal Of Applied Informatics 15, no. 89 (2020): 9–28. http://dx.doi.org/10.37791/2687-0649-2020-15-5-9-28.

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The article describes the functionality developed by the authors of an intelligent software system for optimizing adaptive control of business planning processes in the face of uncertainty. The results are based on a new method for optimizing adaptive project management using network economic and mathematical modeling. Based on this method, a methodology has been developed for solving the problem of optimizing adaptive control of business planning processes, which in the proposed intelligent software decision support system uses a block containing an adaptive control optimization model. As the

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46

Lee, Yung Lung, Shou Jen Hsu, Yen Bin Chen, Yi Wei Chen, and Kuei Yi Chen. "On Study Intelligent Heat Dissipation System." Applied Mechanics and Materials 764-765 (May 2015): 658–63. http://dx.doi.org/10.4028/www.scientific.net/amm.764-765.658.

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This paper develops an Adaptive fuzzy sliding-mode control system algorithm for active heat dissipation system. In the proposed intelligent controller, The adaptive laws adjust the parameters of the fuzzy logic system on-line based on a Lyapunov function, so that the stability of the system can be guaranteed. Additionally, an error estimation mechanism is investigated to estimate the bound of the approximation error. Based on NI-PXI system, this research combined the (TEC) with a duct heater. It designed a smart control system featured by the new active heat dissipation system. It has been pro

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47

Wang, Biyao, Yi Han, Di Tian, and Tian Guan. "Sensor-Based Environmental Perception Technology for Intelligent Vehicles." Journal of Sensors 2021 (September 2, 2021): 1–14. http://dx.doi.org/10.1155/2021/8199361.

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Environmental perception technology is the basis and premise of intelligent vehicle decision control of intelligent vehicles, a crucial link of intelligent vehicles to realize intelligence, and also the basic guarantee of its safety and intelligence. The accuracy and robustness of the perception algorithm will directly affect or even determine the realization of the upper function of intelligent vehicles. The wrong environmental perception will affect the control of the vehicle, thus causing safety risks. This paper discusses the intelligent vehicle perception technology and introduces the dev

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48

Hort, Simon, Bastian Nießing, Sven Jung, and Robert Schmitt. "Smarte Steuerung flexibler Produktionssysteme/Smart control of flexible production systems – A service-oriented approach for the automation of adaptive, individual processes." wt Werkstattstechnik online 110, no. 11-12 (2020): 768–71. http://dx.doi.org/10.37544/1436-4980-2020-11-12-32.

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Um trotz kleiner Stückzahlen kundenindividuelle Produkte effizient herstellen zu können, müssen Produktionssysteme variable Prozesse ausführen und Aufträge optimal einsteuern können. Bei der Umsetzung birgt – neben einer variablen Verkettung der Maschinen – die steuerungsseitige Implementierung große Herausforderungen. Dieser Beitrag stellt sich letzterer Herausforderung und zeigt einen service-orientierten Ansatz zur intelligenten, adaptiven Prozesssteuerung auf. To efficiently manufacture customized products despite small quantities, production systems must be able to execute variable proces

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49

Ma, Kougen, and J. Melcher. "Adaptive Control of Structural Acoustics using Intelligent Structures with Embedded Piezoelectric Patches." Journal of Vibration and Control 9, no. 11 (2003): 1285–302. http://dx.doi.org/10.1177/1077546304030676.

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In this paper we focus on the adaptive control of structural acoustics using intelligent structures with embedded piezoelectric (PZT) patches and low cost digital signal processor systems. After a discussion on the adaptive feedforward control scheme, a hybrid adaptive control scheme is proposed, which takes advantage of both feedback control and adaptive feedforward control. The two schemes are realized on a low-cost, small volume, convenient and universal digital signal processing (DSP) board. A carbon fiber reinforced polymer plate with two embedded PZT patches is developed and used in two

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50

Faravelli, Lucia, Clemente Fuggini, and Filippo Ubertini. "Adaptive Solution for Intelligent Cable Vibration Mitigation." Advances in Science and Technology 56 (September 2008): 137–46. http://dx.doi.org/10.4028/www.scientific.net/ast.56.137.

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The mechanics of cables is caught either by numeric or analytic models which are able to predict the nonlinear dynamic response of this structural elements with the desired level of approximation. Although cable dynamics has been widely explored in the literature, efforts are still required in the field of cable vibration mitigation to conceive an economical, feasible and robust control strategy. An adaptive control strategy combining a distributed passive solution with a semiactive actuation is here proposed for the purpose of reducing the spatial cable vibrations. The effectiveness of the pr

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