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Książki na temat „Multiple model adaptive control”

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Autor: Grafiati
Data publikacji: 25 maja 2024

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1

Jeon, Jung Taek. Multiple model adaptive control with application to DemoDICE. [Downsview, Ont.]: University of Toronto, Institute for Aerospace Studies, 2002.

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2

Jeon, Jung Taek. Multiple model adaptive control with application to DemoDICE. Ottawa: National Library of Canada, 2002.

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3

Datta, Aniruddha. Adaptive Internal Model Control. London: Springer London, 1998. http://dx.doi.org/10.1007/978-0-85729-331-2.

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4

Nguyen, Nhan T. Model-Reference Adaptive Control. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-56393-0.

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Adaptive internal model control. London: Springer, 1998.

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6

Goodwin, Graham, red. Model Identification and Adaptive Control. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0711-8.

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7

Model reference adaptive control of manipulators. Taunton, Somerset, England: Research Studies Press, 1990.

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8

United States. National Aeronautics and Space Administration., red. Model reference adaptive control of robots. Troy, N.Y: Rensselaer Polytechnic Institute, Electrical, Computer and Systems Engineering, 1991.

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9

Roderick, Murray-Smith, i Johansen Tor Arne, red. Multiple model approaches to modelling and control. London: Taylor & Francis, 1997.

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10

Zourntos, Takis. Nonlinear adaptive control based on the related model. Ottawa: National Library of Canada, 1996.

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11

Goodwin, Graham. Model Identification and Adaptive Control: From Windsurfing to Telecommunications. London: Springer London, 2001.

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12

United States. National Aeronautics and Space Administration., red. Adaptive performance seeking control using fuzzy model reference learning control and positive gradient control. Washington, DC: National Aeronautics and Space Administration, 1997.

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13

J, Landy Robert, i Dryden Flight Research Facility, red. Highly integrated digital electronic control, digital flight control, aircraft model identification and adaptive engine control. Edwards, Calif: National Aeronautics and Space Administration, Ames Research Center, Dryden Flight Research Facility, 1987.

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14

Model reference adaptive management theory: Focus on Japanese experiences. [Sapporo, Japan]: Institute of Environmental Creation Internatinal, 2007.

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15

Melvin, James E. AUV fault detection using model based observer residuals. Monterey, Calif: Naval Postgraduate School, 1998.

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16

Control system design based on exact model matching techniques. Berlin: Springer-Verlag, 1985.

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17

Zhen-Lei, Zhou, i Goddard Space Flight Center, red. Position control of redundant manipulators using an adaptive error-based control scheme. Washington, DC: Catholic University of America, Dept. of Electrical Engineering, 1990.

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18

Zhen-Lei, Zhou, i Goddard Space Flight Center, red. Position control of redundant manipulators using an adaptive error-based control scheme. Washington, DC: Catholic University of America, Dept. of Electrical Engineering, 1990.

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19

United States. National Aeronautics and Space Administration., red. Command operator tracker based direct model reference adaptive control of a Puma 560 manipulator. Troy, N.Y: Center for Intelligent Robotic Systems for Space Exploration, Rensselaer Polytechnic Institute, 1992.

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20

Basu, Dipak R. Monetary and financial planning for a transitory economy: An adaptive control model for India. Aldershot, Enland: Avebury, 1995.

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21

Kwak, Se-Hung. Rational behavior model: A tri-level multiple paradigm architecture for robot vehicle control software. Monterey, Calif: Naval Postgraduate School, 1992.

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22

M, McGraw Sandra, i Langley Research Center, red. The multiple-function multi-input/multi-output digital controller system for the AFW wind-tunnel model. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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23

M, McGraw Sandra, i Langley Research Center, red. The multiple-function multi-input/multi-output digital controller system for the AFW wind-tunnel model. Hampton, Va: National Aeronautics and Space Administration, Langley Research Center, 1992.

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24

Model reference adaptive national planning: Focus on Japanese experiences of basic Act of AUGI (NSDI Act). [Sapporo, Japan]: Institute of Environmental Creation Internatinal, 2008.

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25

S, Schkolnik Gerard, i United States. National Aeronautics and Space Administration., red. Flight characteristics of the onboard propulsion system model for the performance seeking control algorithm on an F-15 aircraft. [Washington, DC]: National Aeronautics and Space Administration, 1995.

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26

L, Peddicord K., i Lewis Research Center, red. Investigation and feastibility assessment of TOPAZ-II derivations for space power applications. [Cleveland, Ohio]: National Aeronautics and Space Administration, Lewis Research Center, 1998.

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27

Dubanov, Aleksandr. Computer simulation in pursuit problems. ru: Publishing Center RIOR, 2022. http://dx.doi.org/10.29039/02102-6.

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Streszczenie:
Currently, computer simulation in virtual reality systems has a special status. In order for a computer model to meet the requirements of the tasks it models, it is necessary that the mathematical apparatus correctly describe the simulated phenomena. In this monograph, the simulation of pursuit problems is carried out. An adaptive modeling of the behavior of both pursuers and targets is carried out. An iterative calculation of the trajectories of the participants in the pursuit problem is carried out. The main attention is paid to the methods of pursuit and parallel rendezvous. These methods are taken as the basis of the study and are modified in the future. The scientific novelty of the study is the iterative calculation of the trajectories of the participants in the pursuit task when moving at a constant speed, while following the predicted trajectories. The predicted trajectories form a one-parameter network of continuous lines of the first order of smoothness. The predicted trajectories are calculated taking into account the restrictions on the curvature of the participant in the pursuit problem. The fact of restrictions on curvature can be interpreted as restrictions on the angular frequency of rotation of the object of the pursuit problem. Also, the novelty is the calculation of the iterative process of group pursuit of multiple targets, when targets are hit simultaneously or at specified intervals. The calculation of the parameters of the network of predicted trajectories is carried out with a curvature variation in order to achieve the desired temporal effect. The work also simulates the adaptive behavior of the pursuer and the target. The principle of behavior can be expressed on the example of a pursuer with a simple phrase: "You go to the left - I go to the left." This happens at each iteration step in terms of choosing the direction of rotation. For the purpose, the principle of adaptive behavior is expressed by the phrase: "You go to the left - I go to the right." The studies, algorithms and models presented in the monograph can be in demand in the design of autonomously controlled unmanned aerial vehicles with elements of artificial intelligence. The task models in the monograph are supplemented with many animated images, where you can see the research process. Also, the tasks have an implementation in a computer mathematics system and can be transferred to virtual reality systems if necessary.
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28

Kramarov, Sergey, Vladimir Khramov, Elena Grebenyuk i Anatoly Bocharov. Fundamentals of the ergotechnical approach to the formation of an electronic educational environment. ru: Publishing Center RIOR, 2021. http://dx.doi.org/10.29039/02086-9.

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The monograph deals with the problems of analysis and synthesis of electronic information and learning environment as an ergatic system. The issues of creation, development and evaluation of such systems, as well as information support of the process of building and using artificial intelligence, including in the protection of information, are considered. The monograph provides theoretical justifications for the research of information objects, which reveals the mechanism of constructing a mathematical model of the educational process at the university and explains the understanding of the education system as a system of systems, as well as information processes associated with it. The research presented in the monograph is the basis for the creation of new and effective robotic organizational and organizational-technical systems in education. The monograph is intended for researchers and teachers, postgraduates, undergraduates and engineers specializing in the field of educational management, including intelligent adaptive control of complex robotic systems.
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29

Multiple Model Adaptive Estimation and Control Redistribution for the Vista F-16. Storming Media, 1996.

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30

Flight Control Failure Detection and Control Redistribution Using Multiple Model Adaptive Estimation with Filter Spawning. Storming Media, 2002.

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31

National Aeronautics and Space Administration (NASA) Staff. Identification and Control of Aircrafts Using Multiple Models and Adaptive Critics. Independently Published, 2019.

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32

Multiple Model Adaptive Estimation and Control Redistribution Performance on the VISTA F-16 During Partial Actuator Impairments. Volume 2. Storming Media, 1997.

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33

Multiple Model Adaptive Estimation and Control Redistribution Performance on the VISTA F-16 During Partial Actuator Impairments. Volume 1. Storming Media, 1997.

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34

Multiple Model Adaptive Estimation and Control Redistribution Performance on the Vista F-16 During Partial Actuator Impairments. Volume III. Storming Media, 1997.

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35

Adaptive Internal Model Control. London: Springer London, 1998.

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36

Datta, Aniruddha. Adaptive Internal Model Control. Springer, 2012.

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37

Multiple Model Adaptive Estimation Using Filter Spawning. Storming Media, 1998.

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38

Nguyen, Nhan T. Model-Reference Adaptive Control: A Primer. Springer, 2018.

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39

Nguyen, Nhan T. Model-Reference Adaptive Control: A Primer. Springer International Publishing AG, 2018.

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40

Multiple Model Adaptive Estimation for Time Series Analysis. Storming Media, 2001.

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41

Optimal Design of Generalized Multiple Model Adaptive Controllers. Storming Media, 2004.

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42

Hou, Zhongsheng, i Shangtai Jin. Model Free Adaptive Control: Theory and Applications. Taylor & Francis Group, 2016.

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43

Hou, Zhongsheng, i Shangtai Jin. Model Free Adaptive Control: Theory and Applications. Taylor & Francis Group, 2013.

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44

Little, John D. C. Model of Adaptive Control of Promotional Spending. Creative Media Partners, LLC, 2018.

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45

Hou, Zhongsheng, i Shangtai Jin. Model Free Adaptive Control: Theory and Applications. Taylor & Francis Group, 2013.

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46

Model Free Adaptive Control: Theory and Applications. Taylor & Francis Group, 2013.

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47

Hou, Zhongsheng, i Shangtai Jin. Model Free Adaptive Control: Theory and Applications. Taylor & Francis Group, 2013.

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48

New Algorithms for Moving-Bank Multiple Model Adaptive Estimation. Storming Media, 1998.

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49

Generalized Residual Multiple Model Adaptive Estimation of Parameters and States. Storming Media, 2003.

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50

Serrani, Andrea, Lorenzo Marconi i Alberto Isidori. Robust Autonomous Guidance: An Internal Model Approach. Springer London, Limited, 2012.

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