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Journal articles on the topic 'Control (theory of systems and control)'

Author: Grafiati

Published: 4 June 2021

Last updated: 5 February 2022

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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

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

Nedić, Angelia, and Ji Liu. "Distributed Optimization for Control." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 2018): 77–103. http://dx.doi.org/10.1146/annurev-control-060117-105131.

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Advances in wired and wireless technology have necessitated the development of theory, models, and tools to cope with the new challenges posed by large-scale control and optimization problems over networks. The classical optimization methodology works under the premise that all problem data are available to a central entity (a computing agent or node). However, this premise does not apply to large networked systems, where each agent (node) in the network typically has access only to its private local information and has only a local view of the network structure. This review surveys the development of such distributed computational models for time-varying networks. To emphasize the role of the network structure in these approaches, we focus on a simple direct primal (sub)gradient method, but we also provide an overview of other distributed methods for optimization in networks. Applications of the distributed optimization framework to the control of power systems, least squares solutions to linear equations, and model predictive control are also presented.

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12

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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13

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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14

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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15

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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16

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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17

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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18

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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19

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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20

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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21

HARAMAKI, Shinya, Akihiro HAYASHI, Toshifumi SATAKE, and Shigeru AOMURA. "Distributed Cooperative Control System for Multi-jointed Redundant Manipulator(Control Theory and Application,Session: MA1-B)." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2004.4 (2004): 21. http://dx.doi.org/10.1299/jsmeicam.2004.4.21_2.

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22

Zhao, Yun-Bo, Xi-Ming Sun, Jinhui Zhang, and Peng Shi. "Networked Control Systems: The Communication Basics and Control Methodologies." Mathematical Problems in Engineering 2015 (2015): 1–9. http://dx.doi.org/10.1155/2015/639793.

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As an emerging research field, networked control systems have shown the increasing importance and attracted more and more attention in the recent years. The integration of control and communication in networked control systems has made the design and analysis of such systems a great theoretical challenge for conventional control theory. Such an integration also makes the implementation of networked control systems a necessary intermediate step towards the final convergence of control, communication, and computation. We here introduce the basics of networked control systems and then describe the state-of-the-art research in this field. We hope such a brief tutorial can be useful to inspire further development of networked control systems in both theory and potential applications.

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23

Sampei, Mitsuji. "Control Theory and Industrial Application. Analysis and Control of Mobile Robots using Nonlinear Control Theory. Control Problems in Non-Holonomic Systems." IEEJ Transactions on Industry Applications 114, no. 10 (1994): 955–58. http://dx.doi.org/10.1541/ieejias.114.955.

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24

Elliott, D. L. "Geometric control theory." IEEE Transactions on Automatic Control 45, no. 2 (February 2000): 376–77. http://dx.doi.org/10.1109/tac.2000.839969.

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25

Kondo, Ryou. "Control Theory and Industrial Application. Design of Robust Digital Control Systems." IEEJ Transactions on Industry Applications 114, no. 10 (1994): 947–50. http://dx.doi.org/10.1541/ieejias.114.947.

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26

Darabi, H., M. A. Jafari, and A. L. Buczak. "A control switching theory for supervisory control of discrete event systems." IEEE Transactions on Robotics and Automation 19, no. 1 (February 2003): 131–37. http://dx.doi.org/10.1109/tra.2002.807551.

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27

Recht, Benjamin. "A Tour of Reinforcement Learning: The View from Continuous Control." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 2019): 253–79. http://dx.doi.org/10.1146/annurev-control-053018-023825.

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This article surveys reinforcement learning from the perspective of optimization and control, with a focus on continuous control applications. It reviews the general formulation, terminology, and typical experimental implementations of reinforcement learning as well as competing solution paradigms. In order to compare the relative merits of various techniques, it presents a case study of the linear quadratic regulator (LQR) with unknown dynamics, perhaps the simplest and best-studied problem in optimal control. It also describes how merging techniques from learning theory and control can provide nonasymptotic characterizations of LQR performance and shows that these characterizations tend to match experimental behavior. In turn, when revisiting more complex applications, many of the observed phenomena in LQR persist. In particular, theory and experiment demonstrate the role and importance of models and the cost of generality in reinforcement learning algorithms. The article concludes with a discussion of some of the challenges in designing learning systems that safely and reliably interact with complex and uncertain environments and how tools from reinforcement learning and control might be combined to approach these challenges.

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28

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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29

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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30

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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31

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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32

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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33

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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34

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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35

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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36

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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37

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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38

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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39

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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40

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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41

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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42

Ito, Koji. "Bio-Mimetic Control Systems." Journal of Robotics and Mechatronics 6, no. 1 (February 20, 1994): 109–13. http://dx.doi.org/10.20965/jrm.1994.p0109.

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Bio-mimetic control is intended to study the theoretical framework of autonomous decentralized control systems based on the nonlinear dynamical system theory. It is useful for understanding and designing the parallel-decentralized architecture and the self-organizing function which play an important part in the motor control systems. Based on these theories, research should also be directed toward the analysis of the spatiotemporal motor patterns of the locomotion and arm action as well as toward the application to the sensory-motor coordination and the cooperative control of multiple robots.

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43

YEH, KEN, WEILING CHIANG, and DERSHIN JUANG. "APPLICATION OF FUZZY CONTROL THEORY IN ACTIVE CONTROL OF STRUCTURES." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 04, no. 03 (June 1996): 255–74. http://dx.doi.org/10.1142/s0218488596000160.

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The purpose of this paper is to apply fuzzy control theory in active structural control. A single-degree-of-freedom (SDOF) structure is used to develop the basic approach. The approach is then extended to multi-degree-of-freedom (MDOF) structures with the usage of weighted displacement and weighted velocity. A band-pass white noise or large amount of earthquake records are used as excitations to the structures to calculate the normalized displacements and velocities for obtaining the range of weighted displacements and velocities. Several examples are utilized to demonstrate the feasibility of fuzzy control methodology. It is shown that the fuzzy controller can achieve satisfactory results in the application of active control of structures and the feasibility is verified.

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44

Yang, Tingya, Zhenyu Lu, and Junhao Hu. "H∞Control Theory Using in the Air Pollution Control System." Mathematical Problems in Engineering 2013 (2013): 1–5. http://dx.doi.org/10.1155/2013/145396.

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In recent years, air pollution control has caused great concern. This paper focuses on the primary pollutant SO2in the atmosphere for analysis and control. Two indicators are introduced, which are the concentration of SO2in the emissions (PSO2) and the concentration of SO2in the atmosphere (ASO2). If the ASO2is higher than the certain threshold, then this shows that the air is polluted. According to the uncertainty of the air pollution control systems model,H∞control theory for the air pollution control systems is used in this paper, which can change the PSO2with the method of improving the level of pollution processing or decreasing the emissions, so that air pollution system can maintain robust stability and the indicators ASO2are always operated within the desired target.

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45

Belta, Calin, and Sadra Sadraddini. "Formal Methods for Control Synthesis: An Optimization Perspective." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 2019): 115–40. http://dx.doi.org/10.1146/annurev-control-053018-023717.

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In control theory, complicated dynamics such as systems of (nonlinear) differential equations are controlled mostly to achieve stability. This fundamental property, which can be with respect to a desired operating point or a prescribed trajectory, is often linked with optimality, which requires minimizing a certain cost along the trajectories of a stable system. In formal verification (model checking), simple systems, such as finite-state transition graphs that model computer programs or digital circuits, are checked against rich specifications given as formulas of temporal logics. The formal synthesis problem, in which the goal is to synthesize or control a finite system from a temporal logic specification, has recently received increased interest. In this article, we review some recent results on the connection between optimal control and formal synthesis. Specifically, we focus on the following problem: Given a cost and a correctness temporal logic specification for a dynamical system, generate an optimal control strategy that satisfies the specification. We first provide a short overview of automata-based methods, in which the dynamics of the system are mapped to a finite abstraction that is then controlled using an automaton corresponding to the specification. We then provide a detailed overview of a class of methods that rely on mapping the specification and the dynamics to constraints of an optimization problem. We discuss advantages and limitations of these two types of approaches and suggest directions for future research.

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46

Rubal’skii, G. B. "Stochastic theory of inventory control." Automation and Remote Control 70, no. 12 (December 2009): 2098–108. http://dx.doi.org/10.1134/s0005117909120169.

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47

Jacobs, O. L. R. "Modern control system theory." Automatica 22, no. 2 (March 1986): 258–59. http://dx.doi.org/10.1016/0005-1098(86)90092-0.

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48

KAJIWARA, Hidekazu. "2A1-C05 Synchronization Control of Periodic Input Control Systems by using Entrainment(New Control Theory and Motion Control)." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2012 (2012): _2A1—C05_1—_2A1—C05_2. http://dx.doi.org/10.1299/jsmermd.2012._2a1-c05_1.

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49

AXSATER, SVEN. "Control theory concepts in production and inventory control." International Journal of Systems Science 16, no. 2 (February 1985): 161–69. http://dx.doi.org/10.1080/00207728508926662.

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50

CLARK, JOHN W., DENNIS G. LUCARELLI, and TZYH-JONG TARN. "CONTROL OF QUANTUM SYSTEMS." International Journal of Modern Physics B 17, no. 28 (November 10, 2003): 5397–411. http://dx.doi.org/10.1142/s021797920302051x.

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A quantum system subject to external fields is said to be controllable if these fields can be adjusted to guide the state vector to a desired destination in the state space of the system. Fundamental results on controllability are reviewed against the background of recent ideas and advances in two seemingly disparate endeavours: (i) laser control of chemical reactions and (ii) quantum computation. Using Lie-algebraic methods, sufficient conditions have been derived for global controllability on a finite-dimensional manifold of an infinite-dimensional Hilbert space, in the case that the Hamiltonian and control operators, possibly unbounded, possess a common dense domain of analytic vectors. Some simple examples are presented. A synergism between quantum control and quantum computation is creating a host of exciting new opportunities for both activities. The impact of these developments on computational many-body theory could be profound.

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