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Journal articles on the topic 'Feedback control'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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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 (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 fu

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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 (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 behavio

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3

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 (2018): 145–53. http://dx.doi.org/10.9756/iajse/v5i1/1810013.

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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 (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 (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 (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 (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 (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 (2007): 289–302. http://dx.doi.org/10.1016/j.isatra.2006.12.001.

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11

Jiang, Zekai, Haocheng Lyu, Yuecen Wan, and Haochen Qian. "Sensory Feedback Improvement of BCI Robotics for Movement Control." Applied and Computational Engineering 131, no. 1 (2025): 86–98. https://doi.org/10.54254/2755-2721/2024.20548.

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Brain-computer interfaces have great potential in motor control and rehabilitation. In related research fields, how to effectively monitor users has always been a research focus. Many studies have found that the performance of brain-computer interfaces can be effectively improved by improving and integrating feedback methods. This article reviews the four main types of feedback currently available, including visual feedback, auditory feedback, vibration, and electrical stimulation in tactile feedback, and introduces their principles and applications. This article summarizes the improvements in

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12

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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13

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

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14

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 (2014): 205–9. http://dx.doi.org/10.14511/jcet.2014.040306.

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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 (2008): 769–70. http://dx.doi.org/10.1038/ejhg.2008.56.

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16

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 n

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17

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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18

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 (2008): 643–51. http://dx.doi.org/10.1518/001872008x288583.

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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 (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 (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 (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 (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 (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 (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 i

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26

Hung, J. Y. "Feedback control with Posicast." IEEE Transactions on Industrial Electronics 50, no. 1 (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 (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 (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 (2013): 5–11. http://dx.doi.org/10.1007/s10877-013-9469-y.

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31

Sanfelice, Ricardo G., and Jorge I. Poveda. "Hybrid Feedback Control [Bookshelf]." IEEE Control Systems 45, no. 3 (2025): 78–80. https://doi.org/10.1109/mcs.2025.3554954.

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32

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 (2015): 151–59. http://dx.doi.org/10.18618/rep.2015.2.151159.

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33

Shoureshi, R. "On Passive Implementation of Feedback Control Systems." Journal of Dynamic Systems, Measurement, and Control 111, no. 2 (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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34

Saad, Fawzi AL-Azzawi, and M. Aziz Maysoon. "Strategies of linear feedback control and its classification." TELKOMNIKA Telecommunication, Computing, Electronics and Control 17, no. 4 (2019): 1931–40. https://doi.org/10.12928/TELKOMNIKA.v17i4.10989.

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This paper is concerned with the control problem for a class of nonlinear dynamical (hyperchaotic) systems based on linear feedback control strategies. Since the obtaining positive feedback coefficients are required for these strategies. From this point of view, the available ordinary/dislocated/enhancing and speed feedback control strategies can be classified into two main aspects: control the dynamical systems or can't be control although it own a positive feedback coefficients. So, we focused on these cases, and suggest a new method to recognize which system can be controller it or not.

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35

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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36

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 (2015): 337–42. http://dx.doi.org/10.1080/00207179.2015.1076938.

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37

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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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 (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 dr

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39

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 (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 trans

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40

Dessler, A. E. "Observations of Climate Feedbacks over 2000–10 and Comparisons to Climate Models*." Journal of Climate 26, no. 1 (2013): 333–42. http://dx.doi.org/10.1175/jcli-d-11-00640.1.

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Abstract Feedbacks in response to climate variations during the period 2000–10 have been calculated using reanalysis meteorological fields and top-of-atmosphere flux measurements. Over this period, the climate was stabilized by a strongly negative temperature feedback (~−3 W m−2 K−1); climate variations were also amplified by a strong positive water vapor feedback (~+1.2 W m−2 K−1) and smaller positive albedo and cloud feedbacks (~+0.3 and +0.5 W m−2 K−1, respectively). These observations are compared to two climate model ensembles, one dominated by internal variability (the control ensemble)

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41

YAGHOOBI, HASSAN, and EYAD H. ABED. "LOCAL FEEDBACK CONTROL OF THE NEIMARK–SACKER BIFURCATION." International Journal of Bifurcation and Chaos 13, no. 04 (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 rela

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42

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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43

James, M. R. "Optimal Quantum Control Theory." Annual Review of Control, Robotics, and Autonomous Systems 4, no. 1 (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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44

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

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45

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 (2021): 064102. http://dx.doi.org/10.1063/5.0055925.

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46

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 (2010): 1837–46. http://dx.doi.org/10.2514/1.48844.

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47

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

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48

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

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49

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

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50

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

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