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Journal articles on the topic 'Control'

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Published: 4 June 2021

Last updated: 15 June 2024

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1

Abdulhadi, Sana, Muhammed M. Yakoub, and Ali S. Al- Nuaimi. "The Use of Simulated Acid Rain to Show its Effect on the Morphology of Vicia faba, Educe sativa and Spinacia oleracea and their Uptake of Iron and Potassium." Continuous Research Online Library 1, no. 1 (December 4, 2017): 1–8. http://dx.doi.org/10.28915/control.0002.1.

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2

O. Khalifa, Massoudah, Salah H. Mohamed, Ibtisam M. Ahmadi, Mabrouka I. Abuzeid, Amal O. Basher, Hakma S. Zadan, Kharie M. Ali, and Mabrouka F. Omar. "Effect of Hydrogen Ions and Aluminum Chloride on Isolated Rhizobia from Medicago sativa L." Continuous Research Online Library 1, no. 1 (December 4, 2017): 1–11. http://dx.doi.org/10.28915/control.0003.1.

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3

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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4

Charo, R. Alta, Renate Klein, Janice Raymond, Lynette Dumble, Etienne-Emile Baulieu, and Mort Rosenblum. "Who Controls Birth Control?" Women's Review of Books 9, no. 9 (June 1992): 18. http://dx.doi.org/10.2307/4021278.

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5

Coll-Seck, Awa-Marie. "Who controls malaria control?" Nature 466, no. 7303 (July 2010): 186–87. http://dx.doi.org/10.1038/466186a.

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6

Seminara, Joseph L. "Taking Control of Controls." Ergonomics in Design: The Quarterly of Human Factors Applications 1, no. 3 (July 1993): 21–32. http://dx.doi.org/10.1177/106480469300100309.

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7

Schneider, Dirk. "Border controls: Lipids control proteins and proteins control lipids." Biochimica et Biophysica Acta (BBA) - Biomembranes 1859, no. 4 (April 2017): 507–8. http://dx.doi.org/10.1016/j.bbamem.2016.12.016.

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8

Hewing, Lukas, Kim P. Wabersich, Marcel Menner, and Melanie N. Zeilinger. "Learning-Based Model Predictive Control: Toward Safe Learning in Control." Annual Review of Control, Robotics, and Autonomous Systems 3, no. 1 (May 3, 2020): 269–96. http://dx.doi.org/10.1146/annurev-control-090419-075625.

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Recent successes in the field of machine learning, as well as the availability of increased sensing and computational capabilities in modern control systems, have led to a growing interest in learning and data-driven control techniques. Model predictive control (MPC), as the prime methodology for constrained control, offers a significant opportunity to exploit the abundance of data in a reliable manner, particularly while taking safety constraints into account. This review aims at summarizing and categorizing previous research on learning-based MPC, i.e., the integration or combination of MPC with learning methods, for which we consider three main categories. Most of the research addresses learning for automatic improvement of the prediction model from recorded data. There is, however, also an increasing interest in techniques to infer the parameterization of the MPC controller, i.e., the cost and constraints, that lead to the best closed-loop performance. Finally, we discuss concepts that leverage MPC to augment learning-based controllers with constraint satisfaction properties.

9

J, Lillykutty M., and Dr Rebecca Samson. "The Use of Historical Controls in Post-Test only Non-Equivalent Control Group." International Journal of Trend in Scientific Research and Development Volume-2, Issue-4 (June 30, 2018): 2605–14. http://dx.doi.org/10.31142/ijtsrd15653.

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10

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.

11

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.

12

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.

13

KOLETZKO, SIBYLLE, NIKOLAOS KONSTANTOPOULOS, NORBERT LEHN, and DAVID FORMAN. "Control your controls and conclusions." Archives of Disease in Childhood 84, no. 6 (June 1, 2001): 525.4–525. http://dx.doi.org/10.1136/adc.84.6.525-c.

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14

KOLETZKO, S. "Control your controls and conclusions." Archives of Disease in Childhood 84, no. 6 (June 1, 2001): 525c—525. http://dx.doi.org/10.1136/adc.84.6.525c.

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15

Patterson, Carole H. "Engineering controls vs. infection control." Nursing Management (Springhouse) 32 (June 2001): 29. http://dx.doi.org/10.1097/00006247-200106000-00018.

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16

Newcombe, Nora S. "Some Controls Control Too Much." Child Development 74, no. 4 (July 2003): 1050–52. http://dx.doi.org/10.1111/1467-8624.00588.

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17

McNamee, Daniel, and Daniel M. Wolpert. "Internal Models in Biological Control." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 2019): 339–64. http://dx.doi.org/10.1146/annurev-control-060117-105206.

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Rationality principles such as optimal feedback control and Bayesian inference underpin a probabilistic framework that has accounted for a range of empirical phenomena in biological sensorimotor control. To facilitate the optimization of flexible and robust behaviors consistent with these theories, the ability to construct internal models of the motor system and environmental dynamics can be crucial. In the context of this theoretic formalism, we review the computational roles played by such internal models and the neural and behavioral evidence for their implementation in the brain.

18

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.

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

Lu, Ping. "Tracking Control of Nonlinear Systems with Bounded Controls and Control Rates." IFAC Proceedings Volumes 29, no. 1 (June 1996): 2307–12. http://dx.doi.org/10.1016/s1474-6670(17)58017-9.

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21

Lu, Ping. "Tracking control of nonlinear systems with bounded controls and control rates." Automatica 33, no. 6 (June 1997): 1199–202. http://dx.doi.org/10.1016/s0005-1098(97)00033-2.

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22

Vaassen, E. H. J. "Control en de controllerfunctie." Maandblad Voor Accountancy en Bedrijfseconomie 77, no. 4 (April 1, 2003): 146–54. http://dx.doi.org/10.5117/mab.77.16291.

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Management control en internal control zijn processen, ofwel onderling samenhangende activiteiten die ertoe moeten leiden dat organisaties ‘in control’ komen of blijven. De controller zal in meer of mindere mate steunen op management controls dan wel internal controls. De functie van controller wordt tegenwoordig op verschillende manieren ingevuld. Afhankelijk van de invulling die een bepaalde controller aan zijn functie geeft, zal hij in meer of mindere mate steunen op management control of internal control. De laatste decennia laten een ontwikkeling zien waarin de interne controle is opgeschoven van controle gericht op de betrouwbaarheid van informatie, via controle gericht op de kwaliteit van de bedrijfsvoering, naar beheersing van de processen in organisaties en daarmee het huidige internal control. Daarmee vertoont internal control sterke overeenkomsten met management control. Dit artikel stelt dat, om te voorkomen dat de controller verwordt tot een statische functie die zich niet aanpast aan de veranderingen in de organisatie en haar omgeving, het onderscheid tussen management control en internal control moet worden losgelaten en dat de gesignaleerde convergentietendens tussen management control en internal control nastrevenswaardig is.

23

Goodwin, Matthew L. "Control, Control, Control; Where's Your Control?" Journal of the American College of Surgeons 215, no. 3 (September 2012): 445–46. http://dx.doi.org/10.1016/j.jamcollsurg.2012.06.013.

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24

Han, Shuo, and George J. Pappas. "Privacy in Control and Dynamical Systems." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 2018): 309–32. http://dx.doi.org/10.1146/annurev-control-060117-105018.

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Many modern dynamical systems, such as smart grids and traffic networks, rely on user data for efficient operation. These data often contain sensitive information that the participating users do not wish to reveal to the public. One major challenge is to protect the privacy of participating users when utilizing user data. Over the past decade, differential privacy has emerged as a mathematically rigorous approach that provides strong privacy guarantees. In particular, differential privacy has several useful properties, including resistance to both postprocessing and the use of side information by adversaries. Although differential privacy was first proposed for static-database applications, this review focuses on its use in the context of control systems, in which the data under processing often take the form of data streams. Through two major applications—filtering and optimization algorithms—we illustrate the use of mathematical tools from control and optimization to convert a nonprivate algorithm to its private counterpart. These tools also enable us to quantify the trade-offs between privacy and system performance.

25

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.

26

Hino, Junichi, Masao Kurimoto, and Motomichi Sonobe. "63103 Vibration Control of Truck Crane by Variable Constrained Control with Neural Network(Control of Multibody Systems)." Proceedings of the Asian Conference on Multibody Dynamics 2010.5 (2010): _63103–1_—_63103–8_. http://dx.doi.org/10.1299/jsmeacmd.2010.5._63103-1_.

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27

Gopalakrishnan, Karthik, and Hamsa Balakrishnan. "Control and Optimization of Air Traffic Networks." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (May 3, 2021): 397–424. http://dx.doi.org/10.1146/annurev-control-070720-080844.

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The air transportation system connects the world through the transport of goods and people. However, operational inefficiencies such as flight delays and cancellations are prevalent, resulting in economic and environmental impacts. In the first part of this article, we review recent advances in using network analysis techniques to model the interdependencies observed in the air transportation system and to understand the role of airports in connecting populations, serving air traffic demand, and spreading delays. In the second part, we present some of our recent work on using operational data to build dynamical system models of air traffic delay networks. We show that Markov jump linear system models capture many of the salient characteristics of these networked systems. We illustrate how these models can be validated and then used to analyze system properties such as stability and to design optimal control strategies that limit the propagation of disruptions in air traffic networks.

28

Lafortune, Stéphane. "Discrete Event Systems: Modeling, Observation, and Control." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 2019): 141–59. http://dx.doi.org/10.1146/annurev-control-053018-023659.

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This article begins with an introduction to the modeling of discrete event systems, a class of dynamical systems with discrete states and event-driven dynamics. It then focuses on logical discrete event models, primarily automata, and reviews observation and control problems and their solution methodologies. Specifically, it discusses diagnosability and opacity in the context of partially observed discrete event systems. It then discusses supervisory control for both fully and partially observed systems. The emphasis is on presenting fundamental results first, followed by a discussion of current research directions.

29

Rosolia, Ugo, Xiaojing Zhang, and Francesco Borrelli. "Data-Driven Predictive Control for Autonomous Systems." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 2018): 259–86. http://dx.doi.org/10.1146/annurev-control-060117-105215.

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In autonomous systems, the ability to make forecasts and cope with uncertain predictions is synonymous with intelligence. Model predictive control (MPC) is an established control methodology that systematically uses forecasts to compute real-time optimal control decisions. In MPC, at each time step an optimization problem is solved over a moving horizon. The objective is to find a control policy that minimizes a predicted performance index while satisfying operating constraints. Uncertainty in MPC is handled by optimizing over multiple uncertain forecasts. In this case, performance index and operating constraints take the form of functions defined over a probability space, and the resulting technique is called stochastic MPC. Our research over the past 10 years has focused on predictive control design methods that systematically handle uncertain forecasts in autonomous and semiautonomous systems. In the first part of this article, we present an overview of the approach we use, its main advantages, and its challenges. In the second part, we present our most recent results on data-driven predictive control. We show how to use data to efficiently formulate stochastic MPC problems and autonomously improve performance in repetitive tasks. The proposed framework is able to handle a large set of predicted scenarios in real time and learn from historical data.

30

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.

31

Sasaki, Tatsuya, and Nobuo Ogawa. "OPTICAL CONTROL OF JETS BY RADIATION PRESSURE(Flow Control 2)." Proceedings of the International Conference on Jets, Wakes and Separated Flows (ICJWSF) 2005 (2005): 483–88. http://dx.doi.org/10.1299/jsmeicjwsf.2005.483.

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32

Zhang, Haitao, and Zhen Li. "Fuzzy Immune Control Based Smith Predictor for Networked Control Systems." International Journal of Engineering and Technology 3, no. 1 (2011): 81–84. http://dx.doi.org/10.7763/ijet.2011.v3.204.

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33

Tašner, Tadej, and Darko Lovrec. "Maximum Efficiency Control – A New Strategy To Control Electrohydraulic Systems." Paripex - Indian Journal Of Research 3, no. 5 (January 15, 2012): 107–9. http://dx.doi.org/10.15373/22501991/may2014/34.

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34

MARTINEZ FLORES, MIRNA MARICELA, María Aracelia Alcorta García, SANTOS MENDEZ DIAZ, JOSE ARMANDO SAENZ ESQUEDA, GERARDO MAXIMILIANO MENDEZ, and NORA ELIZONDO VILLAREAL. "TEMPERATURE CONTROL IN AN EVAPORATOR APPLYING RISK-SENSITIVE CONTROL." DYNA 97, no. 4 (July 1, 2022): 345. http://dx.doi.org/10.6036/10498.

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This work designs a stochastic model of an evaporator of a refrigeration system, subject to specific conditions, applying the Risk-Sensitive (R-S) stochastic control equations with tracking, to control the evaporator temperature achieving great energy savings, where the actuator is the expansion valve (EEV).

35

Logan, John R., and Min Zhou. "Do Suburban Growth Controls Control Growth?" American Sociological Review 54, no. 3 (June 1989): 461. http://dx.doi.org/10.2307/2095617.

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36

Rivera, Diego Y. "Designing Soft Controls for Process Control." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 1 (July 2000): 120–23. http://dx.doi.org/10.1177/154193120004400132.

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37

Eletto, D., E. Chevet, Y. Argon, and C. Appenzeller-Herzog. "Redox controls UPR to control redox." Journal of Cell Science 127, no. 17 (August 8, 2014): 3649–58. http://dx.doi.org/10.1242/jcs.153643.

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38

Lancaster, Tony, and Guido Imbens. "Case-control studies with contaminated controls." Journal of Econometrics 71, no. 1-2 (March 1996): 145–60. http://dx.doi.org/10.1016/0304-4076(94)01698-4.

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39

Sepulchre, R., G. Drion, and A. Franci. "Control Across Scales by Positive and Negative Feedback." Annual Review of Control, Robotics, and Autonomous Systems 2, no. 1 (May 3, 2019): 89–113. http://dx.doi.org/10.1146/annurev-control-053018-023708.

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Feedback is a key element of regulation, as it shapes the sensitivity of a process to its environment. Positive feedback upregulates, and negative feedback downregulates. Many regulatory processes involve a mixture of both, whether in nature or in engineering. This article revisits the mixed-feedback paradigm, with the aim of investigating control across scales. We propose that mixed feedback regulates excitability and that excitability plays a central role in multiscale neuronal signaling. We analyze this role in a multiscale network architecture inspired by neurophysiology. The nodal behavior defines a mesoscale that connects actuation at the microscale to regulation at the macroscale. We show that mixed-feedback nodal control provides regulatory principles at the network scale, with a nodal resolution. In this sense, the mixed-feedback paradigm is a control principle across scales.

40

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.

41

Chepuru, Anitha, Dr K. Venugopal Rao, and Amardeep Matta. "Server Access Control." International Journal of Scientific Research 1, no. 7 (June 1, 2012): 78–79. http://dx.doi.org/10.15373/22778179/dec2012/31.

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42

Sutthisa, W. "Biological Control Properties of Cyathus spp. to Control Plant Disease Pathogens." Journal of Pure and Applied Microbiology 12, no. 4 (December 30, 2018): 1755–60. http://dx.doi.org/10.22207/jpam.12.4.08.

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43

Javadi Moghaddam, Jalal, Ghasem Zarei, Davood Momeni, and Hamideh Faridi. "Non-linear control model for use in greenhouse climate control systems." Research in Agricultural Engineering 68, No. 1 (March 23, 2022): 9–17. http://dx.doi.org/10.17221/37/2021-rae.

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In this study, a non-linear control system was designed and proposed to control the greenhouse climate conditions. This control system directly uses the information of sensors, installed inside and outside the greenhouse. To design this proposed control system, the principles of a non-linear control system and the concepts of equilibrium points and zero dynamics of system theories were used. To show the capability and applicability of the proposed control system, it was compared with an integral sliding mode controller. A greenhouse with similar climatic conditions was used to simulate the performance of the integral sliding mode controller. In this study, it was seen that the integral sliding mode control system was more accurate; however, the actuator signals sent by this control system were not smooth. It could damage and depreciate the greenhouse equipment more quickly than the proposed non-linear control system. It was also shown that the regulation of the temperature and humidity was performed very smoothly by changing the reference signals according to the weather conditions outside the greenhouse. The ability of these two control systems was graphically demonstrated for temperature and humidity responses as well as for the signals sent to the actuators.

44

Raghu, S., J. W. Gregory, and J. P. Sullivan. "Modulated High Frequency Fluidic Actuators For Flow Control(Flow Control 2)." Proceedings of the International Conference on Jets, Wakes and Separated Flows (ICJWSF) 2005 (2005): 465–69. http://dx.doi.org/10.1299/jsmeicjwsf.2005.465.

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45

Otto, Samuel E., and Clarence W. Rowley. "Koopman Operators for Estimation and Control of Dynamical Systems." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (May 3, 2021): 59–87. http://dx.doi.org/10.1146/annurev-control-071020-010108.

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A common way to represent a system's dynamics is to specify how the state evolves in time. An alternative viewpoint is to specify how functions of the state evolve in time. This evolution of functions is governed by a linear operator called the Koopman operator, whose spectral properties reveal intrinsic features of a system. For instance, its eigenfunctions determine coordinates in which the dynamics evolve linearly. This review discusses the theoretical foundations of Koopman operator methods, as well as numerical methods developed over the past two decades to approximate the Koopman operator from data, for systems both with and without actuation. We pay special attention to ergodic systems, for which especially effective numerical methods are available. For nonlinear systems with an affine control input, the Koopman formalism leads naturally to systems that are bilinear in the state and the input, and this structure can be leveraged for the design of controllers and estimators.

46

Fligstein, Neil, and Peter Brantley. "Bank Control, Owner Control, or Organizational Dynamics: Who Controls the Large Modern Corporation?" American Journal of Sociology 98, no. 2 (September 1992): 280–307. http://dx.doi.org/10.1086/230009.

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Elacqua, Elizabeth, Stephen J. Koehler, and Jinzhen Hu. "Electronically Governed ROMP: Expanding Sequence Control for Donor–Acceptor Conjugated Polymers." Synlett 31, no. 15 (July 14, 2020): 1435–42. http://dx.doi.org/10.1055/s-0040-1707180.

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Abstract:

Controlling the primary sequence of synthetic polymers remains a grand challenge in chemistry. A variety of methods that exert control over monomer sequence have been realized wherein differential reactivity, pre-organization, and stimuli-response have been key factors in programming sequence. Whereas much has been established in nonconjugated systems, π-extended frameworks remain systems wherein subtle structural changes influence bulk properties. The recent introduction of electronically biased ring-opening metathesis polymerization (ROMP) extends the repertoire of feasible approaches to prescribe donor–acceptor sequences in conjugated polymers, by enabling a system to achieve both low dispersity and controlled polymer sequences. Herein, we discuss recent advances in obtaining well-defined (i.e., low dispersity) polymers featuring donor–acceptor sequence control, and present our design of an electronically ambiguous (4-methoxy-1-(2-ethylhexyloxy) and benzothiadiazole-(donor–acceptor-)based [2.2]paracyclophanediene monomer that undergoes electronically dictated ROMP. The resultant donor–acceptor polymers were well-defined (Đ = 1.2, Mn > 20 k) and exhibited lower energy excitation and emission in comparison to ‘sequence-ill-defined’ polymers. Electronically driven ROMP expands on prior synthetic methods to attain sequence control, while providing a promising platform for further interrogation of polymer sequence and resultant properties.1 Introduction to Sequence Control2 Sequence Control in Polymers3 Multistep-Synthesis-Driven Sequence Control4 Catalyst-Dictated Sequence Control5 Electronically Governed Sequence Control6 Conclusions

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Hamajima, Nobuyuki, Kaoru Hirose, Manami Inoue, Toshiro Takezaki, Testuo Kuroishi, and Kazuo Tajima. "Case-control studies: Matched controls or all available controls?" Journal of Clinical Epidemiology 47, no. 9 (September 1994): 971–75. http://dx.doi.org/10.1016/0895-4356(94)90111-2.

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Sorrell, Ethan, Michael E. Rule, and Timothy O'Leary. "Brain–Machine Interfaces: Closed-Loop Control in an Adaptive System." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (May 3, 2021): 167–89. http://dx.doi.org/10.1146/annurev-control-061720-012348.

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Brain–machine interfaces (BMIs) promise to restore movement and communication in people with paralysis and ultimately allow the human brain to interact seamlessly with external devices, paving the way for a new wave of medical and consumer technology. However, neural activity can adapt and change over time, presenting a substantial challenge for reliable BMI implementation. Large-scale recordings in animal studies now allow us to study how behavioral information is distributed in multiple brain areas, and state-of-the-art interfaces now incorporate models of the brain as a feedback controller. Ongoing research aims to understand the impact of neural plasticity on BMIs and find ways to leverage learning while accommodating unexpected changes in the neural code. We review the current state of experimental and clinical BMI research, focusing on what we know about the neural code, methods for optimizing decoders for closed-loop control, and emerging strategies for addressing neural plasticity.

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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.