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Journal articles on the topic 'Optimal control'

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Author: Grafiati

Published: 4 June 2021

Last updated: 13 July 2024

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1

James, M. R. "Optimal Quantum Control Theory." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (May 3, 2021): 343–67. http://dx.doi.org/10.1146/annurev-control-061520-010444.

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This article explains some fundamental ideas concerning the optimal control of quantum systems through the study of a relatively simple two-level system coupled to optical fields. The model for this system includes both continuous and impulsive dynamics. Topics covered include open- and closed-loop control, impulsive control, open-loop optimal control, quantum filtering, and measurement feedback optimal control.

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2

Iskenderov, A. D., and R. K. Tagiyev. "OPTIMAL CONTROL PROBLEM WITH CONTROLS IN COEFFICIENTS OF QUASILINEAR ELLIPTIC EQUATION." Eurasian Journal of Mathematical and Computer Applications 1, no. 1 (2013): 21–38. http://dx.doi.org/10.32523/2306-3172-2013-1-2-21-38.

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3

Trofimov, A. M., and V. M. Moskovkin. "Optimal control over geomorphological systems." Zeitschrift für Geomorphologie 29, no. 3 (October 31, 1985): 257–63. http://dx.doi.org/10.1127/zfg/29/1985/257.

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4

Goncharenko, Borys, Larysa Vikhrova, and Mariia Miroshnichenko. "Optimal control of nonlinear stationary systems at infinite control time." Central Ukrainian Scientific Bulletin. Technical Sciences, no. 4(35) (2021): 88–93. http://dx.doi.org/10.32515/2664-262x.2021.4(35).88-93.

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The article presents a solution to the problem of control synthesis for dynamical systems described by linear differential equations that function in accordance with the integral-quadratic quality criterion under uncertainty. External perturbations, errors and initial conditions belong to a certain set of uncertainties. Therefore, the problem of finding the optimal control in the form of feedback on the output of the object is presented in the form of a minimum problem of optimal control under uncertainty. The problem of finding the optimal control and initial state, which maximizes the quality criterion, is considered in the framework of the optimization problem, which is solved by the method of Lagrange multipliers after the introduction of the auxiliary scalar function - Hamiltonian. The case of a stationary system on an infinite period of time is considered. The formulas that can be used for calculations are given for the first and second variations. It is proposed to solve the problem of control search in two stages: search of intermediate solution at fixed values of control and error vectors and subsequent search of final optimal control. The solution of -optimal control for infinite time taking into account the signal from the compensator output is also considered, as well as the solution of the corresponding matrix algebraic equations of Ricatti type.

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5

Fahroo, Fariba. "Optimal Control." Journal of Guidance, Control, and Dynamics 24, no. 5 (September 2001): 1054–55. http://dx.doi.org/10.2514/2.4822.

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6

Naidu, D. "Optimal control." IEEE Transactions on Automatic Control 32, no. 10 (October 1987): 944. http://dx.doi.org/10.1109/tac.1987.1104454.

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7

Sargent, R. W. H. "Optimal control." Journal of Computational and Applied Mathematics 124, no. 1-2 (December 2000): 361–71. http://dx.doi.org/10.1016/s0377-0427(00)00418-0.

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8

Piccoli, Benedetto. "Optimal control." Automatica 38, no. 8 (August 2002): 1433–34. http://dx.doi.org/10.1016/s0005-1098(02)00022-5.

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9

Kučera, V., and J. V. Outrata. "Optimal control." Automatica 24, no. 1 (January 1988): 109–10. http://dx.doi.org/10.1016/0005-1098(88)90015-5.

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10

Venkateswarlu, A. "Optimal control." Control Engineering Practice 4, no. 7 (July 1996): 1035–36. http://dx.doi.org/10.1016/0967-0661(96)88552-2.

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11

Becerra, Victor. "Optimal control." Scholarpedia 3, no. 1 (2008): 5354. http://dx.doi.org/10.4249/scholarpedia.5354.

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12

Grimble, M. J. "Optimal Control." IEE Proceedings D Control Theory and Applications 133, no. 6 (1986): 320. http://dx.doi.org/10.1049/ip-d.1986.0056.

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13

Vinter,, R., and S. Sieniutycz,. "Optimal Control." Applied Mechanics Reviews 54, no. 5 (September 1, 2001): B82—B84. http://dx.doi.org/10.1115/1.1399382.

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14

MITA, Tsutomu. "H^|^infin; Optimal Control Implies LQ Optimal Control." Transactions of the Society of Instrument and Control Engineers 24, no. 11 (1988): 1207–9. http://dx.doi.org/10.9746/sicetr1965.24.1207.

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15

Nöldeke, Georg, and Larry Samuelson. "Optimal bunching without optimal control." Journal of Economic Theory 134, no. 1 (May 2007): 405–20. http://dx.doi.org/10.1016/j.jet.2006.01.001.

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16

Oliynyk, Viktor, Fedir Zhuravka, Tetiana Bolgar, and Olha Yevtushenko. "Optimal control of continuous life insurance model." Investment Management and Financial Innovations 14, no. 4 (December 8, 2017): 21–29. http://dx.doi.org/10.21511/imfi.14(4).2017.03.

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The problems of mixed life insurance and insurance in the case of death are considered in the article. The actuarial present value of life insurance is found by solving a system of differential equations. The cases of both constant effective interest rates and variables, depending on the time interval, are examined. The authors used the Pontryagin maximum principle method as the most efficient one, in order to solve the problem of optimal control of the mixed life insurance value. The variable effective interest rate is considered as the control parameter. Some numerical results were given.

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17

Kiviluoto, Sami, Ying Wu, Kai Zenger, and Xiao-Zhi Gao. "2A31 Optimal Control in Reducing Rotor Vibrations." Proceedings of the Symposium on the Motion and Vibration Control 2010 (2010): _2A31–1_—_2A31–14_. http://dx.doi.org/10.1299/jsmemovic.2010._2a31-1_.

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18

Borzì, Alfio, Kazufumi Ito, and Karl Kunisch. "Optimal Control Formulation for Determining Optical Flow." SIAM Journal on Scientific Computing 24, no. 3 (January 2003): 818–47. http://dx.doi.org/10.1137/s1064827501386481.

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19

Bock, Igor, and Ján Lovíšek. "Optimal control problems for variational inequalities with controls in coefficients and in unilateral constraints." Applications of Mathematics 32, no. 4 (1987): 301–14. http://dx.doi.org/10.21136/am.1987.104261.

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20

Ito, Kazufumi. "An Optimal Optical Flow." SIAM Journal on Control and Optimization 44, no. 2 (January 2005): 728–42. http://dx.doi.org/10.1137/s0363012904433444.

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21

Pao, Lucy Y., R. Bulirsch, and D. Kraft. "Computational Optimal Control." Mathematics of Computation 64, no. 212 (October 1995): 1758. http://dx.doi.org/10.2307/2153386.

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22

Bayer, Christian, Denis Belomestny, Paul Hager, Paolo Pigato, John Schoenmakers, and Vladimir Spokoiny. "Reinforced optimal control." Communications in Mathematical Sciences 20, no. 7 (2022): 1951–78. http://dx.doi.org/10.4310/cms.2022.v20.n7.a7.

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23

Hung, Y. S. "H∞Optimal control." International Journal of Control 49, no. 4 (April 1989): 1291–330. http://dx.doi.org/10.1080/00207178908559707.

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24

Hung, Y. S. "H∞Optimal control." International Journal of Control 49, no. 4 (April 1989): 1331–59. http://dx.doi.org/10.1080/00207178908559708.

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25

Naidu,, DS, and I. Kolmanovsky,. "Optimal Control Systems." Applied Mechanics Reviews 57, no. 1 (January 1, 2004): B3—B4. http://dx.doi.org/10.1115/1.1641776.

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26

Vinter, R. B. "Minimax Optimal Control." SIAM Journal on Control and Optimization 44, no. 3 (January 2005): 939–68. http://dx.doi.org/10.1137/s0363012902415244.

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27

Reissig, Gunther, and Matthias Rungger. "Symbolic Optimal Control." IEEE Transactions on Automatic Control 64, no. 6 (June 2019): 2224–39. http://dx.doi.org/10.1109/tac.2018.2863178.

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28

Kuanyi, Zhu. "Optimal Predictive Control." IFAC Proceedings Volumes 26, no. 2 (July 1993): 375–78. http://dx.doi.org/10.1016/s1474-6670(17)49148-8.

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29

Shaw, Robert E., and Thomas F. Carolan. "Adjoint optimal control." Behavioral and Brain Sciences 11, no. 1 (March 1988): 146–47. http://dx.doi.org/10.1017/s0140525x00053267.

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30

Kaczorek, Tadeusz. "H2 optimal control." Control Engineering Practice 5, no. 2 (February 1997): 291–92. http://dx.doi.org/10.1016/s0967-0661(97)90027-7.

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31

Fidler, Jir̆í. "Computational optimal control." Automatica 31, no. 6 (June 1995): 923. http://dx.doi.org/10.1016/0005-1098(95)90012-8.

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32

Filev, Dimiter, and Plamen Angelov. "Fuzzy optimal control." Fuzzy Sets and Systems 47, no. 2 (April 1992): 151–56. http://dx.doi.org/10.1016/0165-0114(92)90172-z.

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33

Castelein, R., and A. Johnson. "Constrained optimal control." IEEE Transactions on Automatic Control 34, no. 1 (1989): 122–26. http://dx.doi.org/10.1109/9.8666.

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34

Kárný, M. "Stochastic Optimal Control." IEE Proceedings D Control Theory and Applications 135, no. 6 (1988): 499. http://dx.doi.org/10.1049/ip-d.1988.0076.

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35

Boucher, Randy, Wei Kang, and Qi Gong. "Galerkin Optimal Control." Journal of Optimization Theory and Applications 169, no. 3 (March 21, 2016): 825–47. http://dx.doi.org/10.1007/s10957-016-0918-x.

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36

Marti, K. "Stochastic optimal structural control: Stochastic optimal open-loop feedback control." Advances in Engineering Software 44, no. 1 (February 2012): 26–34. http://dx.doi.org/10.1016/j.advengsoft.2011.05.040.

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37

Peng, Yong-Bo, Roger Ghanem, and Jie Li. "Generalized optimal control policy for stochastic optimal control of structures." Structural Control and Health Monitoring 20, no. 2 (August 29, 2011): 187–209. http://dx.doi.org/10.1002/stc.483.

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38

Fernández de la Vega, Constanza S., Richard Moore, Mariana Inés Prieto, and Diego Rial. "Optimal control problem for nonlinear optical communications systems." Journal of Differential Equations 346 (February 2023): 347–75. http://dx.doi.org/10.1016/j.jde.2022.11.050.

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39

Casasent, David, and James Jackson. "Laboratory optical linear algebra processor for optimal control." Optics Communications 60, no. 1-2 (October 1986): 1–4. http://dx.doi.org/10.1016/0030-4018(86)90104-5.

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40

Borzì, A., K. Ito, and K. Kunisch. "An optimal control approach to optical flow computation." International Journal for Numerical Methods in Fluids 40, no. 1-2 (August 29, 2002): 231–40. http://dx.doi.org/10.1002/fld.273.

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41

Tsirlin, A. M. "Methods of Simplifying Optimal Control Problems, Heat Exchange and Parametric Control of Oscillators." Nelineinaya Dinamika 18, no. 4 (2022): 0. http://dx.doi.org/10.20537/nd220801.

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Methods of simplifying optimal control problems by decreasing the dimension of the space of states are considered. For this purpose, transition to new phase coordinates or conversion of the phase coordinates to the class of controls is used. The problems of heat exchange and parametric control of oscillators are given as examples: braking/swinging of a pendulum by changing the length of suspension and variation of the energy of molecules’ oscillations in the crystal lattice by changing the state of the medium (exposure to laser radiation). The last problem corresponds to changes in the temperature of the crystal.

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42

ISHIKAWA, Kazuo, and Noboru SAKAMOTO. "Optimal Control for Control Moment Gyros." Transactions of the Society of Instrument and Control Engineers 50, no. 10 (2014): 731–38. http://dx.doi.org/10.9746/sicetr.50.731.

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43

Zhao, Yin, Zhiqiang Li, and Daizhan Cheng. "Optimal Control of Logical Control Networks." IEEE Transactions on Automatic Control 56, no. 8 (August 2011): 1766–76. http://dx.doi.org/10.1109/tac.2010.2092290.

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44

Fornasini, Ettore, and Maria Elena Valcher. "Optimal Control of Boolean Control Networks." IEEE Transactions on Automatic Control 59, no. 5 (May 2014): 1258–70. http://dx.doi.org/10.1109/tac.2013.2294821.

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45

Margaliot, M., and G. Langholz. "Hyperbolic optimal control and fuzzy control." IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans 29, no. 1 (1999): 1–10. http://dx.doi.org/10.1109/3468.736356.

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46

Lou, Hongwei. "Analysis of the Optimal Relaxed Control to an Optimal Control Problem." Applied Mathematics and Optimization 59, no. 1 (May 1, 2008): 75–97. http://dx.doi.org/10.1007/s00245-008-9045-x.

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47

Gammell, Jonathan D., and Marlin P. Strub. "Asymptotically Optimal Sampling-Based Motion Planning Methods." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (May 3, 2021): 295–318. http://dx.doi.org/10.1146/annurev-control-061920-093753.

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Motion planning is a fundamental problem in autonomous robotics that requires finding a path to a specified goal that avoids obstacles and takes into account a robot's limitations and constraints. It is often desirable for this path to also optimize a cost function, such as path length. Formal path-quality guarantees for continuously valued search spaces are an active area of research interest. Recent results have proven that some sampling-based planning methods probabilistically converge toward the optimal solution as computational effort approaches infinity. This article summarizes the assumptions behind these popular asymptotically optimal techniques and provides an introduction to the significant ongoing research on this topic.

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48

Kim, Kyung-Eung. "OPTIMAL CONDITIONS FOR ENDPOINT CONSTRAINED OPTIMAL CONTROL." Bulletin of the Korean Mathematical Society 45, no. 3 (August 31, 2008): 563–71. http://dx.doi.org/10.4134/bkms.2008.45.3.563.

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49

Mikami, Toshio, and Michèle Thieullen. "Optimal Transportation Problem by Stochastic Optimal Control." SIAM Journal on Control and Optimization 47, no. 3 (January 2008): 1127–39. http://dx.doi.org/10.1137/050631264.

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50

Pervadchuk, Vladimir, Daria Vladimirova, Irina Gordeeva, Alex G. Kuchumov, and Dmitrij Dektyarev. "Fabrication of Silica Optical Fibers: Optimal Control Problem Solution." Fibers 9, no. 12 (November 29, 2021): 77. http://dx.doi.org/10.3390/fib9120077.

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In this work, a new approach to solving problems of optimal control of manufacture procedures for the production of silica optical fiber are proposed. The procedure of silica tubes alloying by the Modified Chemical Vapor Deposition (MCVD) method and optical fiber drawing from a preform are considered. The problems of optimal control are presented as problems of controlling distributed systems with objective functionals and controls of different types. Two problems are formulated and solved. The first of them is the problem of the temperature field optimizing in the silica tubes alloying process in controlling the consumption of the oxygen–hydrogen gas mixture (in the one- and two-dimensional statements), the second problem is the geometric optimization of fiber shape in controlling the drawing velocity of the finished fiber. In both problems, while using an analog to the method of Lagrange, the optimality systems in the form of differential problems in partial derivatives are obtained, as well as formulas for finding the optimal control functions in an explicit form. To acquire optimality systems, the qualities of lower semicontinuity, convexity, and objective functional coercivity are applied. The numerical realization of the obtained systems is conducted by using Comsol Multiphysics.

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