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Journal articles on the topic 'Feedback and control'

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Published: 4 June 2021

Last updated: 10 February 2022

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1

Aghaei, Shahin Seyed, and Mohammad Reza Jahed-Motlagh. "Feedback Linearizing Control For Recycled Wastewater Treatment." International Academic Journal of Science and Engineering 05, no. 01 (June 1, 2018): 145–53. http://dx.doi.org/10.9756/iajse/v5i1/1810013.

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2

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.

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3

Korobov, V. I., and T. V. Revina. "On Robust Feedback for Systems with Multidimensional Control." Zurnal matematiceskoj fiziki, analiza, geometrii 13, no. 1 (March 25, 2017): 35–56. http://dx.doi.org/10.15407/mag13.01.035.

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4

Kiselev, O. M. "Stable Feedback Control of a Fast Wheeled Robot." Nelineinaya Dinamika 14, no. 3 (2018): 409–17. http://dx.doi.org/10.20537/nd180310.

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5

Abouelsoud, A. A., Reda Abobeah, and Abdullah Al-odienat. "ADAPTIVE OUTPUT FEEDBACK CONTROL OF CHEMICAL BATCH REACTOR." Journal of Control Engineering and Technology 4, no. 3 (July 30, 2014): 205–9. http://dx.doi.org/10.14511/jcet.2014.040306.

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6

Lee, S., S. M. Meerkov, and T. Runolfsson. "Vibrational Feedback Control." IFAC Proceedings Volumes 20, no. 5 (July 1987): 139–44. http://dx.doi.org/10.1016/s1474-6670(17)55023-5.

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7

Giovanini, Leonardo L. "Predictive feedback control." ISA Transactions 42, no. 2 (April 2003): 207–26. http://dx.doi.org/10.1016/s0019-0578(07)60127-x.

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8

Astrom, K. J. "Adaptive feedback control." Proceedings of the IEEE 75, no. 2 (1987): 185–217. http://dx.doi.org/10.1109/proc.1987.13721.

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9

Giovanini, Leonardo. "Cooperative-feedback control." ISA Transactions 46, no. 3 (June 2007): 289–302. http://dx.doi.org/10.1016/j.isatra.2006.12.001.

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10

Trentelman, Harry L. "Feedback control systems." Automatica 32, no. 6 (June 1996): 945–46. http://dx.doi.org/10.1016/0005-1098(96)89428-3.

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11

Kheir, Naim A. "Feedback control systems." Automatica 22, no. 6 (November 1986): 765. http://dx.doi.org/10.1016/0005-1098(86)90021-x.

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12

Phillips, Charles L., and Royce D. Harbor. "Feedback control systems." Automatica 26, no. 4 (July 1990): 824–25. http://dx.doi.org/10.1016/0005-1098(90)90061-l.

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13

HIGUCHI, Takehiro, Seiya UENO, and Takuya OHMURA. "A3 Singularity Avoidance for Control Moment Gyro Systems Using State Feedback Control Law." Proceedings of the Space Engineering Conference 2009.18 (2010): 11–16. http://dx.doi.org/10.1299/jsmesec.2009.18.11.

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14

Sguarezi Filho, Joãozinho, André Luiz de Lacerda Ferreira Murari, Carlos Eduardo Capovilla, José Alberto Torrico Altuna, and Rogério Vani Jacomini. "A State Feedback Dfig Power Control For Wind Generation." Eletrônica de Potência 20, no. 2 (May 1, 2015): 151–59. http://dx.doi.org/10.18618/rep.2015.2.151159.

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15

Heldin, Carl-Henrik, Johan Lennartsson, and Carina Hellberg. "Feedback Control: The role of negative feedback in signal transduction control." European Journal of Human Genetics 16, no. 7 (April 9, 2008): 769–70. http://dx.doi.org/10.1038/ejhg.2008.56.

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16

Neth, Hansjörg, Sangeet S. Khemlani, and Wayne D. Gray. "Feedback Design for the Control of a Dynamic Multitasking System: Dissociating Outcome Feedback From Control Feedback." Human Factors: The Journal of the Human Factors and Ergonomics Society 50, no. 4 (August 2008): 643–51. http://dx.doi.org/10.1518/001872008x288583.

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17

Hung, J. Y. "Feedback control with Posicast." IEEE Transactions on Industrial Electronics 50, no. 1 (February 2003): 94–99. http://dx.doi.org/10.1109/tie.2002.804979.

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18

Milne, Stewart E., and Gavin N. C. Kenny. "Feedback control of anaesthesia." Current Opinion in Anaesthesiology 11, no. 6 (November 1998): 659–63. http://dx.doi.org/10.1097/00001503-199811000-00012.

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19

Moin, Parviz, and Thomas Bewley. "Feedback Control of Turbulence." Applied Mechanics Reviews 47, no. 6S (June 1, 1994): S3—S13. http://dx.doi.org/10.1115/1.3124438.

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A brief review of current approaches to active feedback control of the fluctuations arising in turbulent flows is presented, emphasizing the mathematical techniques involved. Active feedback control schemes are categorized and compared by examining the extent to which they are based on the governing flow equations. These schemes are broken down into the following categories: adaptive schemes, schemes based on heuristic physical arguments, schemes based on a dynamical systems approach, and schemes based on optimal control theory applied directly to the Navier-Stokes equations. Recent advances in methods of implementing small scale flow control ideas are also reviewed.

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20

Durham, Joseph, and Jeff Moehlis. "Feedback control of canards." Chaos: An Interdisciplinary Journal of Nonlinear Science 18, no. 1 (March 2008): 015110. http://dx.doi.org/10.1063/1.2804554.

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21

Folcher, J. P., M. Carbillet, A. Ferrari, and A. Abelli. "Adaptive Optics Feedback Control." EAS Publications Series 59 (2013): 93–130. http://dx.doi.org/10.1051/eas/1359006.

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22

Zbikowski, R. "Sensor-rich feedback control." IEEE Instrumentation and Measurement Magazine 7, no. 3 (September 2004): 19–26. http://dx.doi.org/10.1109/mim.2004.1337909.

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23

Wei, R. Y., R. D. Johnston, and R. M. Wood. "Enhanced multiloop feedback control." International Journal of Control 49, no. 4 (April 1989): 1195–216. http://dx.doi.org/10.1080/00207178908559701.

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24

Martin, Robert L. "Phase-cancellation feedback control." Hearing Journal 59, no. 10 (October 2006): 56. http://dx.doi.org/10.1097/01.hj.0000286010.77703.f0.

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25

Asbury, A. John. "Feedback control in anaesthesia." International journal of clinical monitoring and computing 14, no. 1 (February 1997): 1–10. http://dx.doi.org/10.1007/bf03356572.

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26

Engell, Sebastian. "Robust multivariable feedback control." Automatica 27, no. 4 (July 1991): 749–50. http://dx.doi.org/10.1016/0005-1098(91)90070-i.

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27

Dumont, Guy A. "Feedback control for clinicians." Journal of Clinical Monitoring and Computing 28, no. 1 (April 12, 2013): 5–11. http://dx.doi.org/10.1007/s10877-013-9469-y.

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28

Hanan, Avi, Adam Jbara, and Arie Levant. "Homogeneous Output-Feedback Control." IFAC-PapersOnLine 53, no. 2 (2020): 5081–86. http://dx.doi.org/10.1016/j.ifacol.2020.12.1119.

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29

Shoureshi, R. "On Passive Implementation of Feedback Control Systems." Journal of Dynamic Systems, Measurement, and Control 111, no. 2 (June 1, 1989): 339–42. http://dx.doi.org/10.1115/1.3153057.

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Closed-loop control systems, especially linear quadratic regulators (LQR), require feedbacks of all states. This requirement may not be feasible for those systems which have limitations due to geometry, power, required sensors, size, and cost. To overcome such requirements a passive method for implementation of state feedback control systems is presented.

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30

AZLAN, Norsinnira, and Hiroshi YAMAURA. "20204 Study of Feedback Error Learning Control for Underactuated Systems." Proceedings of Conference of Kanto Branch 2009.15 (2009): 149–50. http://dx.doi.org/10.1299/jsmekanto.2009.15.149.

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31

Rubió-Massegú, Josep, Francisco Palacios-Quiñonero, Josep M. Rossell, and Hamid Reza Karimi. "Static Output-Feedback Control for Vehicle Suspensions: A Single-Step Linear Matrix Inequality Approach." Mathematical Problems in Engineering 2013 (2013): 1–12. http://dx.doi.org/10.1155/2013/907056.

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In this paper, a new strategy to design static output-feedback controllers for a class of vehicle suspension systems is presented. A theoretical background on recent advances in output-feedback control is first provided, which makes possible an effective synthesis of static output-feedback controllers by solving a single linear matrix inequality optimization problem. Next, a simplified model of a quarter-car suspension system is proposed, taking the ride comfort, suspension stroke, road holding ability, and control effort as the main performance criteria in the vehicle suspension design. The new approach is then used to design a static output-feedbackH∞controller that only uses the suspension deflection and the sprung mass velocity as feedback information. Numerical simulations indicate that, despite the restricted feedback information, this static output-feedbackH∞controller exhibits an excellent behavior in terms of both frequency and time responses, when compared with the corresponding state-feedbackH∞controller.

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32

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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33

Tian-Yu, Liu, Cao Jia-Hui, Liu Yan-Yan, Gao Tian-Fu, and Zheng Zhi-Gang. "Optimal control of temperature feedback control ratchets." Acta Physica Sinica 70, no. 19 (2021): 190501. http://dx.doi.org/10.7498/aps.70.20210517.

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34

Li, Fangfei, and Zhaoxu Yu. "Feedback control and output feedback control for the stabilisation of switched Boolean networks." International Journal of Control 89, no. 2 (August 20, 2015): 337–42. http://dx.doi.org/10.1080/00207179.2015.1076938.

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35

Kress-Gazit, Hadas, Morteza Lahijanian, and Vasumathi Raman. "Synthesis for Robots: Guarantees and Feedback for Robot Behavior." Annual Review of Control, Robotics, and Autonomous Systems 1, no. 1 (May 28, 2018): 211–36. http://dx.doi.org/10.1146/annurev-control-060117-104838.

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Robot control for tasks such as moving around obstacles or grasping objects has advanced significantly in the last few decades. However, controlling robots to perform complex tasks is still accomplished largely by highly trained programmers in a manual, time-consuming, and error-prone process that is typically validated only through extensive testing. Formal methods are mathematical techniques for reasoning about systems, their requirements, and their guarantees. Formal synthesis for robotics refers to frameworks for specifying tasks in a mathematically precise language and automatically transforming these specifications into correct-by-construction robot controllers or into a proof that the task cannot be done. Synthesis allows users to reason about the task specification rather than its implementation, reduces implementation error, and provides behavioral guarantees for the resulting controller. This article reviews the current state of formal synthesis for robotics and surveys the landscape of abstractions, specifications, and synthesis algorithms that enable it.

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36

Tagawa, Yukihiro, Michiko Watanabe, Masashi Furukawa, Hiromitsu Hikita, and Ryozaburo Tagawa. "Application of GA to CAD on Feedback Control Systems(Design and Control 1,Session: MP1-B)." Abstracts of the international conference on advanced mechatronics : toward evolutionary fusion of IT and mechatronics : ICAM 2004.4 (2004): 27. http://dx.doi.org/10.1299/jsmeicam.2004.4.27_2.

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37

YAGHOOBI, HASSAN, and EYAD H. ABED. "LOCAL FEEDBACK CONTROL OF THE NEIMARK–SACKER BIFURCATION." International Journal of Bifurcation and Chaos 13, no. 04 (April 2003): 879–93. http://dx.doi.org/10.1142/s0218127403006972.

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Local bifurcation control designs have been addressed in the literature for stationary, Hopf, and period doubling bifurcations. This paper addresses the local feedback control of the Neimark–Sacker bifurcation, in which an invariant closed curve emerges from a nominal fixed point of a discrete-time system as a parameter is slowly varied. The analysis of this bifurcation is more involved than for previously considered bifurcations. The paper develops the stability and amplitude equations for the bifurcated invariant curves of the Neimark–Sacker bifurcation, and then proceeds to apply these relationships in the design of nonlinear feedbacks. The feedback controllers are applied to two examples: the delayed logistic map and a model reference adaptive control system model.

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38

Peresada, S., S. Kovbasa, S. Korol, and N. Zhelinskyi. "FEEDBACK LINEARIZING FIELD-ORIENTED CONTROL OF INDUCTION GENERATOR: THEORY AND EXPERIMENTS." Tekhnichna Elektrodynamika 2017, no. 2 (March 15, 2017): 48–56. http://dx.doi.org/10.15407/techned2017.02.048.

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39

Mohan, Krithika, Tianzhi Luo, Douglas N. Robinson, and Pablo A. Iglesias. "Cell shape regulation through mechanosensory feedback control." Journal of The Royal Society Interface 12, no. 109 (August 2015): 20150512. http://dx.doi.org/10.1098/rsif.2015.0512.

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Cells undergo controlled changes in morphology in response to intracellular and extracellular signals. These changes require a means for sensing and interpreting the signalling cues, for generating the forces that act on the cell's physical material, and a control system to regulate this process. Experiments on Dictyostelium amoebae have shown that force-generating proteins can localize in response to external mechanical perturbations. This mechanosensing, and the ensuing mechanical feedback, plays an important role in minimizing the effect of mechanical disturbances in the course of changes in cell shape, especially during cell division, and likely in other contexts, such as during three-dimensional migration. Owing to the complexity of the feedback system, which couples mechanical and biochemical signals involved in shape regulation, theoretical approaches can guide further investigation by providing insights that are difficult to decipher experimentally. Here, we present a computational model that explains the different mechanosensory and mechanoresponsive behaviours observed in Dictyostelium cells. The model features a multiscale description of myosin II bipolar thick filament assembly that includes cooperative and force-dependent myosin–actin binding, and identifies the feedback mechanisms hidden in the observed mechanoresponsive behaviours of Dictyostelium cells during micropipette aspiration experiments. These feedbacks provide a mechanistic explanation of cellular retraction and hence cell shape regulation.

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40

Trottemant, E. J., C. W. Scherer, M. Weiss, and A. Vermeulen. "Robust Missile Feedback Control Strategies." Journal of Guidance, Control, and Dynamics 33, no. 6 (November 2010): 1837–46. http://dx.doi.org/10.2514/1.48844.

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41

Yaesh, Isaac, Agnès Cohen, and Uri Shaked. "Delayed State-Feedback H ∞ Control." IFAC Proceedings Volumes 31, no. 19 (July 1998): 51–56. http://dx.doi.org/10.1016/s1474-6670(17)41127-x.

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42

Murtoviita, E., and R. Ylinen. "Manipulator Control by Vision Feedback." IFAC Proceedings Volumes 19, no. 9 (June 1986): 117–22. http://dx.doi.org/10.1016/s1474-6670(17)57518-7.

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43

Rocha, Paula. "Feedback control of multidimensional behaviors." Systems & Control Letters 45, no. 3 (March 2002): 207–15. http://dx.doi.org/10.1016/s0167-6911(01)00178-5.

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44

Hunt, K. J., H. Gollee, R. Jaime, and N. Donaldson. "Feedback control of unsupported standing." Technology and Health Care 7, no. 6 (December 1, 1999): 443–47. http://dx.doi.org/10.3233/thc-1999-7610.

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45

Savina, T. V., A. A. Nepomnyashchy, S. Brandon, A. A. Golovin, and D. R. Lewin. "Feedback control of morphological instability." Journal of Crystal Growth 237-239 (April 2002): 178–80. http://dx.doi.org/10.1016/s0022-0248(01)01872-3.

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46

Pyragas, Kestutis. "Delayed feedback control of chaos." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 364, no. 1846 (July 27, 2006): 2309–34. http://dx.doi.org/10.1098/rsta.2006.1827.

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Time-delayed feedback control is well known as a practical method for stabilizing unstable periodic orbits embedded in chaotic attractors. The method is based on applying feedback perturbation proportional to the deviation of the current state of the system from its state one period in the past, so that the control signal vanishes when the stabilization of the target orbit is attained. A brief review on experimental implementations, applications for theoretical models and most important modifications of the method is presented. Recent advancements in the theory, as well as an idea of using an unstable degree of freedom in a feedback loop to avoid a well-known topological limitation of the method, are described in detail.

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47

Chakraborty, Debraj, and Jacob Hammer. "Optimal control during feedback failure." International Journal of Control 82, no. 8 (June 18, 2009): 1448–68. http://dx.doi.org/10.1080/00207170802510265.

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48

Allison, Andrew, and Derek Abbott. "Control systems with stochastic feedback." Chaos: An Interdisciplinary Journal of Nonlinear Science 11, no. 3 (September 2001): 715–24. http://dx.doi.org/10.1063/1.1397769.

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49

Lu, Chenyang, John A. Stankovic, Tarek Abdelzaher, Sang H. Son, and Gang Tao. "Feedback control real-time scheduling." ACM SIGOPS Operating Systems Review 34, no. 2 (April 2000): 33. http://dx.doi.org/10.1145/346152.346222.

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50

Lu, Chenyang, John A. Stankovic, Tarek Abdelzaher, Sang H. Son, and Gang Tao. "Feedback control real-time scheduling." ACM SIGOPS Operating Systems Review 34, no. 2 (April 2000): 40. http://dx.doi.org/10.1145/346152.346275.

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