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Journal articles on the topic 'Systems Theory and Control'

Author: Grafiati

Published: 4 June 2021

Last updated: 19 February 2022

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1

Chen, Can, Amit Surana, Anthony M. Bloch, and Indika Rajapakse. "Multilinear Control Systems Theory." SIAM Journal on Control and Optimization 59, no. 1 (January 2021): 749–76. http://dx.doi.org/10.1137/19m1262589.

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2

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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3

Junge, Oliver, and Jan Lunze. "Control Theory of Networked Systems." at - Automatisierungstechnik 61, no. 7 (July 2013): 455–56. http://dx.doi.org/10.1524/auto.2013.9007.

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4

Li, Fuhuo. "Control Systems and Number Theory." International Journal of Mathematics and Mathematical Sciences 2012 (2012): 1–28. http://dx.doi.org/10.1155/2012/508721.

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We try to pave a smooth road to a proper understanding of control problems in terms of mathematical disciplines, and partially show how to number-theorize some practical problems. Our primary concern is linear systems from the point of view of our principle of visualization of the state, an interface between the past and the present. We view all the systems as embedded in the state equation, thus visualizing the state. Then we go on to treat the chain-scattering representation of the plant of Kimura 1997, which includes the feedback connection in a natural way, and we consider theH∞-control problem in this framework. We may view in particular the unit feedback system as accommodated in the chain-scattering representation, giving a better insight into the structure of the system. Its homographic transformation works as the action of the symplectic group on the Siegel upper half-space in the case of constant matrices. Both ofH∞- and PID-controllers are applied successfully in the EV control by J.-Y. Cao and B.-G. Cao 2006 and Cao et al. 2007, which we may unify in our framework. Finally, we mention some similarities between control theory and zeta-functions.

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5

Trentelman, HL, AA Stoorvogel, M. Hautus, and L. Dewell. "Control Theory for Linear Systems." Applied Mechanics Reviews 55, no. 5 (September 1, 2002): B87. http://dx.doi.org/10.1115/1.1497472.

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6

Ros, Javier, Alberto Casas, Jasiel Najera, and Isidro Zabalza. "64048 QUANTITATIVE FEEDBACK THEORY CONTROL OF A HEXAGLIDE TYPE PARALLEL MANIPULATOR(Control of Multibody Systems)." Proceedings of the Asian Conference on Multibody Dynamics 2010.5 (2010): _64048–1_—_64048–10_. http://dx.doi.org/10.1299/jsmeacmd.2010.5._64048-1_.

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7

Marden, Jason R., and Jeff S. Shamma. "Game Theory and Control." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 2018): 105–34. http://dx.doi.org/10.1146/annurev-control-060117-105102.

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Game theory is the study of decision problems in which there are multiple decision makers and the quality of a decision maker's choice depends on both that choice and the choices of others. While game theory has been studied predominantly as a modeling paradigm in the mathematical social sciences, there is a strong connection to control systems in that a controller can be viewed as a decision-making entity. Accordingly, game theory is relevant in settings with multiple interacting controllers. This article presents an introduction to game theory, followed by a sampling of results in three specific control theory topics where game theory has played a significant role: ( a) zero-sum games, in which the two competing players are a controller and an adversarial environment; ( b) team games, in which several controllers pursue a common goal but have access to different information; and ( c) distributed control, in which both a game and online adaptive rules are designed to enable distributed interacting subsystems to achieve a collective objective.

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8

Shadwick, William F. "Differential Systems and Nonlinear Control Theory." IFAC Proceedings Volumes 28, no. 14 (June 1995): 721–29. http://dx.doi.org/10.1016/s1474-6670(17)46914-x.

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9

LIN, JING-YUE, and ZI-HOU YANG. "Mathematical Control Theory of Singular Systems." IMA Journal of Mathematical Control and Information 6, no. 2 (1989): 189–98. http://dx.doi.org/10.1093/imamci/6.2.189.

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10

Buxey, Geoff. "Inventory control systems: theory and practice." International Journal of Information and Operations Management Education 1, no. 2 (2006): 158. http://dx.doi.org/10.1504/ijiome.2006.009173.

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11

Shamma, Jeff S. "Game theory, learning, and control systems." National Science Review 7, no. 7 (November 4, 2019): 1118–19. http://dx.doi.org/10.1093/nsr/nwz163.

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Summary Game theory is the study of interacting decision makers, whereas control systems involve the design of intelligent decision-making devices. When many control systems are interconnected, the result can be viewed through the lens of game theory. This article discusses both long standing connections between these fields as well as new connections stemming from emerging applications.

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12

Lyshevski,, SE, and PJ Eagle,. "Control Systems Theory with Engineering Applications." Applied Mechanics Reviews 55, no. 2 (March 1, 2002): B28—B29. http://dx.doi.org/10.1115/1.1451163.

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13

van der Schaft, Arjan. "Port-Hamiltonian Modeling for Control." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 393–416. http://dx.doi.org/10.1146/annurev-control-081219-092250.

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This article provides a concise summary of the basic ideas and concepts in port-Hamiltonian systems theory and its use in analysis and control of complex multiphysics systems. It gives special attention to new and unexplored research directions and relations with other mathematical frameworks. Emergent control paradigms and open problems are indicated, including the relation with thermodynamics and the question of uniting the energy-processing view of control, as emphasized by port-Hamiltonian systems theory, with a complementary information-processing viewpoint.

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14

Schweizer, Jörg, and Michael Peter Kennedy. "Predictive Poincaré control: A control theory for chaotic systems." Physical Review E 52, no. 5 (November 1, 1995): 4865–76. http://dx.doi.org/10.1103/physreve.52.4865.

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15

Szabó, Zoltán. "Geometric Control Theory and Linear Switched Systems." European Journal of Control 15, no. 3-4 (January 2009): 249–59. http://dx.doi.org/10.3166/ejc.15.249-259.

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16

KOBAYASHI, Koichi. "Systems and Control Theory for IoT Era." IEICE ESS Fundamentals Review 11, no. 3 (2018): 172–79. http://dx.doi.org/10.1587/essfr.11.3_172.

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17

Butkovskiy, A. G., A. V. Babichev, N. L. Lepe, and I. Ju Chkhiqvadze. "Geometric Theory of Dynamic Systems with Control." IFAC Proceedings Volumes 23, no. 8 (August 1990): 273–80. http://dx.doi.org/10.1016/s1474-6670(17)51928-x.

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18

Sobolev, V. A. "Geometrical Theory of Singularly Perturbed Control Systems." IFAC Proceedings Volumes 23, no. 8 (August 1990): 415–20. http://dx.doi.org/10.1016/s1474-6670(17)51951-5.

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19

Rosa, Marta, Gabriel Gil, Stefano Corni, and Roberto Cammi. "Quantum optimal control theory for solvated systems." Journal of Chemical Physics 151, no. 19 (November 21, 2019): 194109. http://dx.doi.org/10.1063/1.5125184.

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20

Curtain, Ruth F. "Optimal control theory for infinite dimensional systems." Automatica 33, no. 4 (April 1997): 750–51. http://dx.doi.org/10.1016/s0005-1098(97)85780-9.

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21

Kliem, W. R. "Symmetrizable Systems in Mechanics and Control Theory." Journal of Applied Mechanics 59, no. 2 (June 1, 1992): 454–56. http://dx.doi.org/10.1115/1.2899543.

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Stability investigations of nonconservative systems MX¨ + BX˙ + CX = 0 in mechanics and control theory become substantially easier if the coefficient matrices B and C are either both real symmetric or both complex symmetric. It is therefore of interest to give conditions under which, by means of a similarity transformation, a system may be converted into one of these forms. We discuss the following questions: Are such systems robust with respect to perturbations in the entries of the coefficient matrices? Do relevant applications exist?

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22

Petersen, I. R. "Control theory for linear systems [Book Review]." IEEE Transactions on Automatic Control 48, no. 3 (March 2003): 526. http://dx.doi.org/10.1109/tac.2003.809170.

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23

Žampa, Pavel. "A New Approach to Control Systems Theory." IFAC Proceedings Volumes 30, no. 12 (July 1997): 177–82. http://dx.doi.org/10.1016/s1474-6670(17)42786-8.

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24

Gershon, E., and U. Shaked. "H∞ feedback-control theory in biochemical systems." International Journal of Robust and Nonlinear Control 18, no. 1 (2007): 14–50. http://dx.doi.org/10.1002/rnc.1195.

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25

Myshlyaev, L. P., V. F. Evtushenko, K. A. Ivushkin, and G. V. Makarov. "Development of similarity theory for control systems." IOP Conference Series: Materials Science and Engineering 354 (May 2018): 012005. http://dx.doi.org/10.1088/1757-899x/354/1/012005.

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26

Zhang, Weihai, Honglei Xu, Huanqing Wang, and Zhongwei Lin. "Stochastic Systems and Control: Theory and Applications." Mathematical Problems in Engineering 2017 (2017): 1–4. http://dx.doi.org/10.1155/2017/4063015.

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27

Zadeh, L. A. "Stochastic finite-state systems in control theory." Information Sciences 251 (December 2013): 1–9. http://dx.doi.org/10.1016/j.ins.2013.06.039.

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28

Veliov, Vladimir M. "Optimal control of heterogeneous systems: Basic theory." Journal of Mathematical Analysis and Applications 346, no. 1 (October 2008): 227–42. http://dx.doi.org/10.1016/j.jmaa.2008.05.012.

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29

Al-Towaim, T., A. D. Barton, P. L. Lewin, E. Rogers *, and D. H. Owens. "Iterative learning control — 2D control systems from theory to application." International Journal of Control 77, no. 9 (June 10, 2004): 877–93. http://dx.doi.org/10.1080/00207170410001726778.

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30

NONAMI, Kenzo, Jan Wei WANG, and Shouji YAMAZAKI. "Spillover control of magnetic levitation systems using H.INF. control theory." Transactions of the Japan Society of Mechanical Engineers Series C 57, no. 534 (1991): 568–75. http://dx.doi.org/10.1299/kikaic.57.568.

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31

HOTZ, ANTHONY, and ROBERT E. SKELTON. "Covariance control theory." International Journal of Control 46, no. 1 (July 1987): 13–32. http://dx.doi.org/10.1080/00207178708933880.

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32

Madhav, Manu S., and Noah J. Cowan. "The Synergy Between Neuroscience and Control Theory: The Nervous System as Inspiration for Hard Control Challenges." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 243–67. http://dx.doi.org/10.1146/annurev-control-060117-104856.

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Here, we review the role of control theory in modeling neural control systems through a top-down analysis approach. Specifically, we examine the role of the brain and central nervous system as the controller in the organism, connected to but isolated from the rest of the animal through insulated interfaces. Though biological and engineering control systems operate on similar principles, they differ in several critical features, which makes drawing inspiration from biology for engineering controllers challenging but worthwhile. We also outline a procedure that the control theorist can use to draw inspiration from the biological controller: starting from the intact, behaving animal; designing experiments to deconstruct and model hierarchies of feedback; modifying feedback topologies; perturbing inputs and plant dynamics; using the resultant outputs to perform system identification; and tuning and validating the resultant control-theoretic model using specially engineered robophysical models.

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33

Lefkowitz, I. "Applied control theory." Automatica 21, no. 1 (January 1985): 110–11. http://dx.doi.org/10.1016/0005-1098(85)90104-9.

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34

Hollis, Karen L. "Strategies for integrating biological theory, control systems theory, and Pavlovian conditioning." Behavioral and Brain Sciences 23, no. 2 (April 2000): 258–59. http://dx.doi.org/10.1017/s0140525x00322439.

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To make possible the integration proposed by Domjan et al., psychologists first need to close the research gap between behavioral ecology and the study of Pavlovian conditioning. I suggest two strategies, namely, to adopt more behavioral ecological approaches to social behavior or to co-opt problems already addressed by behavioral ecologists that are especially well suited to the study of Pavlovian conditioning.

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35

Mansour, Mohammed. "Systems theory and human science." Annual Reviews in Control 26, no. 1 (January 2002): 1–13. http://dx.doi.org/10.1016/s1367-5788(02)80004-9.

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36

Němcová, Jana, Mihály Petreczky, and Jan H. van Schuppen. "Realization Theory of Nash Systems." SIAM Journal on Control and Optimization 51, no. 5 (January 2013): 3386–414. http://dx.doi.org/10.1137/110847482.

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37

Gonçalves, J. Basto. "Realization Theory for Hamiltonian Systems." SIAM Journal on Control and Optimization 25, no. 1 (January 1987): 63–73. http://dx.doi.org/10.1137/0325005.

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38

Allgöwer, Frank, Vincent Blondel, and Uwe Helmke. "Control Theory: Mathematical Perspectives on Complex Networked Systems." Oberwolfach Reports 9, no. 1 (2012): 661–732. http://dx.doi.org/10.4171/owr/2012/12.

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39

Hosoe, Shigeyuki. "Synthesis of servo systems by modern control theory." IEEJ Transactions on Industry Applications 107, no. 8 (1987): 960–64. http://dx.doi.org/10.1541/ieejias.107.960.

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40

Lions, J.-L. "Boundary Control of Hyperbolic Systems and Homogenization Theory." IFAC Proceedings Volumes 18, no. 2 (June 1985): 95–101. http://dx.doi.org/10.1016/s1474-6670(17)60920-0.

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41

YANG, Wen. "Supervisory Control Theory of Fuzzy Discrete Event Systems." Acta Automatica Sinica 34, no. 4 (March 2, 2009): 460–65. http://dx.doi.org/10.3724/sp.j.1004.2008.00460.

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42

Meerkov, S. M., and T. Runolfsson. "Theory of Aiming Control for Linear Stochastic Systems." IFAC Proceedings Volumes 23, no. 8 (August 1990): 43–47. http://dx.doi.org/10.1016/s1474-6670(17)51981-3.

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43

Butkovskiy, A. G. "Geometric Theory of Dynamic Systems with Control (CDS)." IFAC Proceedings Volumes 22, no. 4 (June 1989): 289–93. http://dx.doi.org/10.1016/s1474-6670(17)53559-4.

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44

Bose, Bimal K. "Fuzzy control of industrial systems — theory and applications." Automatica 37, no. 6 (June 2001): 958–59. http://dx.doi.org/10.1016/s0005-1098(01)00041-3.

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45

Maschke, R. Lozano, B. Brogliato, O. Egeland an. "Dissipative Systems Analysis and Control. Theory and Applications." Measurement Science and Technology 12, no. 12 (November 15, 2001): 2211. http://dx.doi.org/10.1088/0957-0233/12/12/703.

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46

Engell, S., E. J. Davison, S. Engell, K. Malinowski, and G. Schmidt. "The Future of Control Theory for Complex Systems." IFAC Proceedings Volumes 20, no. 9 (August 1987): 81–83. http://dx.doi.org/10.1016/s1474-6670(17)55684-0.

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47

Sengupta, Raja, and Stéphane Lafortune. "An Optimal Control Theory for Discrete Event Systems." SIAM Journal on Control and Optimization 36, no. 2 (March 1998): 488–541. http://dx.doi.org/10.1137/s0363012994260957.

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48

Burke, Peter J. "Extending Identity Control Theory: Insights from Classifier Systems." Sociological Theory 22, no. 4 (December 2004): 574–94. http://dx.doi.org/10.1111/j.0735-2751.2004.00234.x.

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49

Elkin, V. I. "Geometric Theory of Reduction of Nonlinear Control Systems." Computational Mathematics and Mathematical Physics 58, no. 2 (February 2018): 155–58. http://dx.doi.org/10.1134/s0965542518020045.

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50

Montúfar, Guido, Keyan Ghazi-Zahedi, and Nihat Ay. "A Theory of Cheap Control in Embodied Systems." PLOS Computational Biology 11, no. 9 (September 1, 2015): e1004427. http://dx.doi.org/10.1371/journal.pcbi.1004427.

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