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Добірка наукової літератури з теми "Control theory"

Автор: Grafiati

Опубліковано: 4 червня 2021

Оновлено: 9 листопада 2024

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Статті в журналах з теми "Control theory"

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

HERSHBERGER, WAYNE A. "Control Theory and Learning Theory." American Behavioral Scientist 34, no. 1 (September 1990): 55–66. http://dx.doi.org/10.1177/0002764290034001006.

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4

Shin, Seiichi. "Motion Control and Control Theory." IEEJ Transactions on Industry Applications 120, no. 1 (2000): 3–4. http://dx.doi.org/10.1541/ieejias.120.3.

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5

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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6

Cziko, Gary A. "Perceptual Control Theory." Educational Researcher 21, no. 9 (December 1992): 25–27. http://dx.doi.org/10.3102/0013189x021009025.

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7

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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8

Osipova, J. V. "Identity control theory." Moscow State University Bulletin. Series 18. Sociology and Political Science 22, no. 1 (January 1, 2016): 215–24. http://dx.doi.org/10.24290/1029-3736-2016-22-1-215-224.

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9

Hannon, Bruce. "Ecosystem control theory." Journal of Theoretical Biology 121, no. 4 (August 1986): 417–37. http://dx.doi.org/10.1016/s0022-5193(86)80100-x.

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10

Melzack, Ronald. "Gate control theory." Pain Forum 5, no. 2 (June 1996): 128–38. http://dx.doi.org/10.1016/s1082-3174(96)80050-x.

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Дисертації з теми "Control theory"

1

Oh, Sunseek. "A Promising Control Theory." University of Arizona Linguistics Circle, 1989. http://hdl.handle.net/10150/226550.

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2

Spjøtvold, Jørgen. "Parametric Programming in Control Theory." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-2198.

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Анотація:

The main contributions in this thesis are advances in parametric programming. The thesis is divided into three parts; theoretical advances, application areas and constrained control allocation. The first part deals with continuity properties and the structure of solutions to convex parametric quadratic and linear programs. The second part focuses on applications of parametric quadratic and linear programming in control theory. The third part deals with constrained control allocation and how parametric programming can be used to obtain explicit solutions to this problem.

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3

Zimbidis, Alexandros A. "Control theory and insurance systems." Thesis, City University London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.287673.

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4

Ovenden, Christopher David. "Towards a theory of control." Thesis, University of Manchester, 2013. https://www.research.manchester.ac.uk/portal/en/theses/towards-a-theory-of-control(ecf7fcd2-5ccd-45e7-a02e-86202c390057).html.

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Control is a concept that has received surprisingly little attention in the philosophy of action and ethics, given its prima facie ties to freedom, responsibility, intentionality and agency more generally. In this collection I take the first step towards an account of agential control: the kind of control that agents commonly exercise over actions, events, and even other agents. In the introduction I give a sketch of the complete thesis on control: characterising agential control as consisting primarily in the restriction or guidance of some process, and secondarily in the continuous monitoring of that same process. I go on to suggest that the primary aspect of control involves an agent’s having the ability to effectively intervene in the process that they are controlling. The collection itself consists of three journal style papers that, whilst not being focussed explicitly on control themselves, begin to fill out the sketch in my introduction: roughly, I think that control requires an ability to intervene (effectively, an ability to do otherwise), I think that ability should be understood as a kind of disposition to effectively intervene in a process should an agent try, and I think that to build a satisfactory conditional account of dispositions we need to appeal to the recently proposed contextualist account of dispositions from David Manley and Ryan Wasserman. The three papers aim to support each of these thoughts: The first paper, ‘The Anti-Akrasia Chip’, presents a counterexample to the well-known Fischer-Ravizza account of guidance control and suggests that what that account lacks is an emphasis on an agent’s being able to effectively intervene in their own behaviour; the second paper, ‘Getting Specific with Manley and Wasserman’, uses a novel counterexample to motivate a particular reading on Manley and Wasserman’s contextualist account of dispositions; and the final paper, ‘Relevant Abilities’, involves a defence of dispositional compatibilism by introducing the notion of a relevant ability: one grounded by the contextualist account of dispositions developed in the previous paper.

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5

Robertz, Daniel. "Formal computational methods for control theory." [S.l.] : [s.n.], 2006. http://deposit.ddb.de/cgi-bin/dokserv?idn=981070019.

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6

Agi, Egemen. "Mathematical Modeling Of Gate Control Theory." Master's thesis, METU, 2009. http://etd.lib.metu.edu.tr/upload/3/12611468/index.pdf.

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The purpose of this thesis work is to model the gate control theory, which explains the modulation of pain signals, with a motivation of finding new possible targets for pain treatment and to find novel control algorithms that can be used in engineering practice. The difference of the current study from the previous modeling trials is that morphologies of neurons that constitute gate control system are also included in the model by which structure-function relationship can be observed. Model of an excitable neuron is constructed and the response of the model for different perturbations are investigated. The simulation results of the excitable cell model is obtained and when compared with the experimental findings obtained by using crayfish, it is found that they are in good agreement. Model encodes stimulation intensity information as firing frequency and also it can add sub-threshold inputs and fire action potentials as real neurons. Moreover, model is able to predict depolarization block. Absolute refractory period of the single cell model is found as 3.7 ms. The developed model, produces no action potentials when the sodium channels are blocked by tetrodotoxin. Also, frequency and amplitudes of generated action potentials increase when the reversal potential of Na is increased. In addition, propagation of signals along myelinated and unmyelinated fibers is simulated and input current intensity-frequency relationships for both type of fibers are constructed. Myelinated fiber starts to conduct when current input is about 400 pA whereas this minimum threshold value for unmyelinated fiber is around 1100 pA. Propagation velocity in the 1 cm long unmyelinated fiber is found as 0.43 m/s whereas velocity along myelinated fiber with the same length is found to be 64.35 m/s. Developed synapse model exhibits the summation and tetanization properties of real synapses while simulating the time dependency of neurotransmitter concentration in the synaptic cleft. Morphometric analysis of neurons that constitute gate control system are done in order to find electrophysiological properties according to dimensions of the neurons. All of the individual parts of the gate control system are connected and the whole system is simulated. For different connection configurations, results of the simulations predict the observed phenomena for the suppression of pain. If the myelinated fiber is dissected, the projection neuron generates action potentials that would convey to brain and elicit pain. However, if the unmyelinated fiber is dissected, projection neuron remains silent. In this study all of the simulations are preformed using Simulink.

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7

Bellon, James. "Riccati Equations in Optimal Control Theory." Digital Archive @ GSU, 2008. http://digitalarchive.gsu.edu/math_theses/46.

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It is often desired to have control over a process or a physical system, to cause it to behave optimally. Optimal control theory deals with analyzing and finding solutions for optimal control for a system that can be represented by a set of differential equations. This thesis examines such a system in the form of a set of matrix differential equations known as a continuous linear time-invariant system. Conditions on the system, such as linearity, allow one to find an explicit closed form finite solution that can be more efficiently computed compared to other known types of solutions. This is done by optimizing a quadratic cost function. The optimization leads to solving a Riccati equation. Conditions are discussed for which solutions are possible. In particular, we will obtain a solution for a stable and controllable system. Numerical examples are given for a simple system with 2x2 matrix coefficients.

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8

Yiu, Ka Fai Cedric. "Aerodynamic design via optimal control theory." Thesis, University of Oxford, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.317867.

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9

Al-Assaf, Y. "Self-tuning control : Theory and applications." Thesis, University of Oxford, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.235033.

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10

Abouhajar, Alaa Abdulwahab Abdulrahman. "Function theory related to H∞ control." Thesis, University of Newcastle Upon Tyne, 2012. http://hdl.handle.net/10443/1412.

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We define Γ(E), a subset of C³, related to the structured singular value μ of 2x2 matrices. μ is used to analyse performance and robustness of linear feedback systems in control engineering. We find a characterisation for the elements of Γ(E) and establish a necessary and sufficient condition for the existence of an analytic function from the unit disc into Γ(E) satisfying an arbitrary finite number of interpolation conditions. We prove a Schwarz Lemma for Γ(E) when one of the points in Γ(E) is (0,0,0), then we show that in this case, the Carathéodory and Kobayashi distances between the two points in Γ(E) coincide. We also give a characterisation of the interior, the topological boundary and the distinguished boundary of Γ(E), then we define Γ(E)-inner functions and show that if there exists an analytic function from the unit disc into Γ(E) that satisfies the interpolating conditions, then there is a rational Γ(E)-inner function that interpolates.

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Книги з теми "Control theory"

1

Leigh, J. R. Control Theory. 2nd ed. Stevenage: IET, 2004.

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2

Engineers, Institution of Electrical, ed. Control theory. 2nd ed. London: Institution of Electrical Engineers, 2004.

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3

Doyle, John Comstock. Feedback control theory. New York: Macmillan Pub. Co., 1992.

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4

Sethi, Suresh P. Optimal Control Theory. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91745-6.

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5

Baillieul, J., and J. C. Willems, eds. Mathematical Control Theory. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1416-8.

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6

Sontag, Eduardo D. Mathematical Control Theory. New York, NY: Springer New York, 1998. http://dx.doi.org/10.1007/978-1-4612-0577-7.

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7

Zabczyk, Jerzy. Mathematical Control Theory. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-44778-6.

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8

Foias, Ciprian, Bruce Francis, J. William Helton, Huibert Kwakernaak, and J. Boyd Pearson. H∞-Control Theory. Edited by Edoardo Mosca and Luciano Pandolfi. Berlin, Heidelberg: Springer Berlin Heidelberg, 1991. http://dx.doi.org/10.1007/bfb0084465.

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9

Zabczyk, Jerzy. Mathematical Control Theory. Boston, MA: Birkhäuser Boston, 2008. http://dx.doi.org/10.1007/978-0-8176-4733-9.

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10

Sontag, Eduardo D. Mathematical Control Theory. New York, NY: Springer US, 1990. http://dx.doi.org/10.1007/978-1-4684-0374-9.

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Частини книг з теми "Control theory"

1

Elaydi, Saber N. "Control Theory." In An Introduction to Difference Equations, 251–95. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4757-3110-1_6.

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2

Pommaret, J. F. "Control Theory." In Partial Differential Equations and Group Theory, 319–89. Dordrecht: Springer Netherlands, 1994. http://dx.doi.org/10.1007/978-94-017-2539-2_8.

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3

Manitius, Andre Z. "Control Theory." In Encyclopedia of Operations Research and Management Science, 275–80. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_163.

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4

Davis, Mark H. A. "Control theory." In Markov Models and Optimization, 134–85. Boston, MA: Springer US, 1993. http://dx.doi.org/10.1007/978-1-4899-4483-2_4.

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5

Taha, Walid M., Abd-Elhamid M. Taha, and Johan Thunberg. "Control Theory." In Cyber-Physical Systems: A Model-Based Approach, 57–78. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-36071-9_4.

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6

Piccoli, B. "Control Theory." In Foliations on Surfaces, 391–98. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-04524-4_17.

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7

Elaydi, Saber N. "Control Theory." In An Introduction to Difference Equations, 259–302. New York, NY: Springer New York, 1996. http://dx.doi.org/10.1007/978-1-4757-9168-6_6.

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8

Ding, Suining. "Control Theory." In Environment-Behavior Studies for Healthcare Design, 173–200. New York: Routledge, 2022. http://dx.doi.org/10.4324/9781003177029-9.

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9

Boucher, Thomas O. "Control Theory." In Computer Automation in Manufacturing, 172–239. Boston, MA: Springer US, 1996. http://dx.doi.org/10.1007/978-1-4615-2045-0_5.

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10

Tyson, Robert K., and Benjamin W. Frazier. "Control Theory." In Principles of Adaptive Optics, 221–68. 5th ed. Boca Raton: CRC Press, 2022. http://dx.doi.org/10.1201/9781003140191-8.

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Тези доповідей конференцій з теми "Control theory"

1

Riess, Hans, Gregory Henselman-Petrusek, Michael C. Munger, Robert Ghrist, Zachary I. Bell, and Michael M. Zavlanos. "Network Preference Dynamics Using Lattice Theory." In 2024 American Control Conference (ACC), 2802–8. IEEE, 2024. http://dx.doi.org/10.23919/acc60939.2024.10645007.

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2

Mekki, Ahmed, and Simon Collart-Dutilleul. "Graph theory: Application to system recovery." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334718.

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3

Farshchi, Seyyed Mohammad Reza, and Irene Kehvazadeh. "Finding risk dominance strategy in imperfect game, a theory perspective." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334732.

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4

Kawahata, Shotaro, Mingcong Deng, and Shin Wakitani. "Operator theory based nonlinear fault tolerance control for MIMO microreactor." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737598.

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5

Dellar, Oliver J., and Bryn Ll Jones. "Discretising the linearised navier-stokes equations: A systems theory approach." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737634.

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6

Meng, Fanlin, Jinya Su, Cunjia Liu, and Wen-Hua Chen. "Dynamic decision making in lane change: Game theory with receding horizon." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737643.

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7

Stocco, Leo, Ignacio Galiano, Francisco Paz, Roberto Rosales, and David Feixo. "Control Theory Practices Applied to Teaching Practical Control Theory." In ASME 2017 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/imece2017-72290.

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Control theory is useful in a broad range of diverse applications that include mechanical, electrical and chemical systems. This work extends the application of control theory from achieving a desired technical outcome to achieving a desired pedagogical outcome. In this paper, the desired outcome is the teaching and learning of applied control systems theory. In the proposed model, the student is the plant, their skill set is the set point, the instructor is the controller, and either an exam or a project-based-learning (PBL) course is the sensor. The PBL course is used to evaluate the actual skill set of the students and the difference between the actual and desired skill set (error signal) is fed back to the professor to initiate curricular changes. This model is shown to be applicable at various levels. At the micro level, examinations within a course are used to optimize that course. At the intermediate level, a PBL course is used as the sensor for a conventional lecture-based course. At the macro level, the departmental program as a whole is matched to the needs of industry in pursuit of a 100% employment rate of its students.

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8

Moskowitz, Ira S., Pedro N. Safier, and Paul Cotae. "Algebraic Information Theory and Kosko's Forbidden Interval Theorem." In Modelling, Identification and Control. Calgary,AB,Canada: ACTAPRESS, 2013. http://dx.doi.org/10.2316/p.2013.801-053.

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9

JONES, JR., JOHN. "Nonlinear control theory." In 4th Symposium on Multidisciplinary Analysis and Optimization. Reston, Virigina: American Institute of Aeronautics and Astronautics, 1992. http://dx.doi.org/10.2514/6.1992-4740.

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10

"Session 5: System theory and control theory." In 2010 International Conference on Intelligent Computing and Integrated Systems (ICISS). IEEE, 2010. http://dx.doi.org/10.1109/iciss.2010.5656954.

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Звіти організацій з теми "Control theory"

1

Dai, Xingde. Conference on Operator Theory, Wavelet Theory and Control Theory. Fort Belvoir, VA: Defense Technical Information Center, September 1993. http://dx.doi.org/10.21236/ada277429.

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2

Levis, Alexander H., and Michael Athans. Command and Control Theory. Fort Belvoir, VA: Defense Technical Information Center, August 1989. http://dx.doi.org/10.21236/ada212515.

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3

Wiatrowski, Michael. Social Control Theory and Delinquency. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.857.

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4

Doyle, John C. Robust Control Theory and Applications. Fort Belvoir, VA: Defense Technical Information Center, February 1998. http://dx.doi.org/10.21236/ada337888.

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5

Bobashev, Georgiy, John Holloway, Eric Solano, and Boris Gutkin. A Control Theory Model of Smoking. RTI Press, June 2017. http://dx.doi.org/10.3768/rtipress.2017.op.0040.1706.

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Анотація:

We present a heuristic control theory model that describes smoking under restricted and unrestricted access to cigarettes. The model is based on the allostasis theory and uses a formal representation of a multiscale opponent process. The model simulates smoking behavior of an individual and produces both short-term (“loading up” after not smoking for a while) and long-term smoking patterns (e.g., gradual transition from a few cigarettes to one pack a day). By introducing a formal representation of withdrawal- and craving-like processes, the model produces gradual increases over time in withdrawal- and craving-like signals associated with abstinence and shows that after 3 months of abstinence, craving disappears. The model was programmed as a computer application allowing users to select simulation scenarios. The application links images of brain regions that are activated during the binge/intoxication, withdrawal, or craving with corresponding simulated states. The model was calibrated to represent smoking patterns described in peer-reviewed literature; however, it is generic enough to be adapted to other drugs, including cocaine and opioids. Although the model does not mechanistically describe specific neurobiological processes, it can be useful in prevention and treatment practices as an illustration of drug-using behaviors and expected dynamics of withdrawal and craving during abstinence.

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6

Seidman, Thomas I. Control Theory and Distributed Parameter Systems. Fort Belvoir, VA: Defense Technical Information Center, January 1986. http://dx.doi.org/10.21236/ada182808.

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7

Doyle, John C. Nonlinear Robust Control Theory and Applications. Fort Belvoir, VA: Defense Technical Information Center, November 1998. http://dx.doi.org/10.21236/ada367040.

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8

Doyle, John C. Nonlinear Robust Control Theory and Applications. Fort Belvoir, VA: Defense Technical Information Center, January 1997. http://dx.doi.org/10.21236/ada336238.

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9

Morton, Blaise, and Michael Elgersma. Nonlinear Control Theory for Aerospace Vehicles. Fort Belvoir, VA: Defense Technical Information Center, March 1998. http://dx.doi.org/10.21236/ada341388.

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10

Baillieul, John. Brockettfest: A Conference on Mathematical Control Theory. Fort Belvoir, VA: Defense Technical Information Center, September 1999. http://dx.doi.org/10.21236/ada370105.

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