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Journal articles on the topic 'Feedback control'

Author: Grafiati

Published: 4 June 2021

Last updated: 10 March 2023

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1

Liaw, Der-Cherng, Li-Feng Tsai, and Jun-Wei Chen. "Feedback Control Design for VCM." International Journal of Electronics and Electrical Engineering 9, no. 1 (March 2021): 16–20. http://dx.doi.org/10.18178/ijeee.9.1.16-20.

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A design of PD feedback control law for a class of the second order system to satisfy the desired performance requirements is presented. It is achieved by using root-locus approach. The desired specifications of the step-input system response are first transformed into a required region for the poles of the PD control closed-loop system. Ranges of the corresponding PD control gains are then derived to guarantee the poles of the closed-loop system lie within the targeted region. Besides, the proposed control law is also applied to the feedback control of Voice Coil Motor (VCM) to support the function of the so-called “Optical Image Stabilization (OIS).” Numerical results by using a referenced VCM model have demonstrated the success of the proposed design.

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2

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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3

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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4

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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5

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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6

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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7

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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8

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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9

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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10

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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11

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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12

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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13

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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14

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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15

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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16

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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17

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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18

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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19

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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20

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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21

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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22

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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23

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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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

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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26

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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27

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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28

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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29

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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30

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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31

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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32

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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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

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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36

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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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

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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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

Novák, Béla, P. K. Vinod, Paula Freire, and Orsolya Kapuy. "Systems-level feedback in cell-cycle control." Biochemical Society Transactions 38, no. 5 (September 24, 2010): 1242–46. http://dx.doi.org/10.1042/bst0381242.

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Alternation of chromosome replication and segregation is essential for successful completion of the cell cycle and it requires an oscillation of Cdk1 (cyclin-dependent kinase 1)–CycB (cyclin B) activity. In the present review, we illustrate the essential features of checkpoint controlled and uncontrolled cell-cycle oscillations by using mechanical metaphors. Despite variations in the molecular details of the oscillatory mechanism, the underlying network motifs responsible for the oscillations are always well-conserved. The checkpoint-controlled cell cycles are always driven by a negative-feedback loop amplified by double-negative feedbacks (antagonism).

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41

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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42

Dear, Alexander J., Thomas C. T. Michaels, Tuomas P. J. Knowles, and L. Mahadevan. "Feedback control of protein aggregation." Journal of Chemical Physics 155, no. 6 (August 14, 2021): 064102. http://dx.doi.org/10.1063/5.0055925.

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43

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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44

Qazi, Ihsan Ayyub, Lachlan L. H. Andrew, and Taieb Znati. "Congestion Control With Multipacket Feedback." IEEE/ACM Transactions on Networking 20, no. 6 (December 2012): 1721–33. http://dx.doi.org/10.1109/tnet.2012.2188838.

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45

ABDUL-WAHAB, ABDUL-AMIR A., and M. A. ZOHDY. "Eigensystem assignment by feedback control." International Journal of Control 50, no. 5 (November 1989): 1619–34. http://dx.doi.org/10.1080/00207178908953455.

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46

Feliachi, A. "On linear output feedback control." IEEE Transactions on Circuits and Systems 33, no. 4 (April 1986): 450–52. http://dx.doi.org/10.1109/tcs.1986.1085921.

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47

Waller, Michael H. "Book Review: Feedback Control Systems." International Journal of Electrical Engineering & Education 23, no. 4 (October 1986): 377–78. http://dx.doi.org/10.1177/002072098602300432.

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48

Andrews, C. W. "Book Review: Feedback Control Systems." International Journal of Electrical Engineering & Education 26, no. 3 (July 1989): 276–77. http://dx.doi.org/10.1177/002072098902600331.

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49

WAIT, C. M., V. A. GOAT, and C. E. BLOGG. "Feedback control of neuromuscular blockade." Anaesthesia 42, no. 11 (November 1987): 1212–17. http://dx.doi.org/10.1111/j.1365-2044.1987.tb05233.x.

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50

Andersson, S. B., and D. Hristu. "Symbolic Feedback Control for Navigation." IEEE Transactions on Automatic Control 51, no. 6 (June 2006): 926–37. http://dx.doi.org/10.1109/tac.2006.876956.

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