Relevant bibliographies by topics / Feedback control

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Feedback control'

Author: Grafiati
Published: 4 June 2021
Last updated: 10 March 2023

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Journal articles on the topic "Feedback control"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Feedback control"

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Malmqvist, Andreas. "Robotsystem with feedback control." Thesis, University West, Department of Technology, Mathematics and Computer Science, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:hv:diva-1348.

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As a part of ongoing research projects at University West in the area of robot welding applications, a study was looked-for in the field of feedback control of robot systems with external sensors. A literature survey was performed in this field. A virtual instrument was developed in a PC using the LabView software from National Instruments. The instrument receives a signal from a force sensor, converts the input data and sends out the computed signal on a configured serial port. This information is then received by the robot system to be used to control the robot trajectory. The system was tested and it showed that external closed loop control of robot movement was possible to do. Problems with high delay time in the ABB IRB2400 robot system limits the bandwidth or the speed of the closed loop system. This delay time is caused by the intrinsic offset function that is needed to change robot path during motion. This function requires a great computational cost. The conclusion is that ABB IRB2400 robots, with the S4 controller, are limited for applications with low bandwidth because of their motion computation program structure. It does not allow for external feedback applications that require higher robot movement speeds.

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Rafaely, Boaz. "Feedback control of sound." Thesis, University of Southampton, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.390329.

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Sinclair, Jeff. "Feedback control for exergames." Thesis, Edith Cowan University, Research Online, Perth, Western Australia, 2011. https://ro.ecu.edu.au/theses/380.

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The concept of merging exercise equipment with video games, known as exergaming, has the potential to be one of the main tools used in addressing the current rising obesity epidemic. Existing research shows that exergaming can help improve fitness and additionally motivate people to become more active. The two key elements of attractiveness - how much people want to play or use the exergaming system; and effectiveness – how effective the exergaming system is in actually increasing or maintaining physical fitness, need to be maximised to obtain the best outcomes from an exergaming system; we put this forward as the Dual Flow Model. As part of the development of our exergame system we required the use of a heart rate response simulator. We discovered that there was no existing quantitative model appropriate for the simulation of heart rate responses to exercise. In order to overcome this, we developed our own model for the simulation of heart rate response. Based on our model, we developed a simulation tool known as the Virtual Body Simulator, which we used during our exergame development. Subsequent verification of the model using the trial data indicated that the model accurately represented exergame player heart rate responses to a level that was more than sufficient for exergame research and development. In our experiment, attractiveness was controlled by manipulation of the game difficulty to match the skill of the player. The balance of challenge and skills to facilitate the attainment of the flow state, as described by Csikszentmihalyi (1975), is widely accepted as a motivator for various activities. Effectiveness, in our experiments was controlled through exercise intensity. Exercise intensity was adjusted based on the player‟s heart rate to maintain intensity within the limits of the ASCM Guidelines (ACSM, 2006) for appropriate exercise intensity levels. We tested the Dual Flow Model by developing an exergame designed to work in four different modes; created by selectively varying the control mechanisms for exercise workout intensity and game mental challenge. We then ran a trial with 21 subjects who used the exergame system in each of the different modes. The trial results in relation to the Dual Flow Model showed that we developed an excellent intensity control system based on heart rate monitoring; successfully managing workout intensity for the subjects. However, we found that the subjects generally found the intensity controlled sessions less engaging, being closer to the flow state in the sessions where the intensity was controlled based on heart rate. The dynamic difficulty adjustment system developed for our exergame also did not appear to help facilitate attainment of the flow state. Various theories are put forward as to why this may have occurred. We did find that challenge control had an impact on the actual intensity of the workout. When the intensity was not managed, the challenge control modes were generally closer to the desired heart rates. While the difference was not statistically very large, there was a strong correlation between the intensity of the different modes. This correlation was also present when looking at the players‟ perception of intensity, indicating that the difference was enough to be noticed by the subjects.
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Moghaddami, Khalilzad Nima. "Hierarchical Scheduling and Feedback Control." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-12127.

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Hierarchical scheduling provides predictable timing and temporal isolation; two properties desirable in real-time embedded systems. In hierarchically scheduled systems, subsystems should receive a sufficient amount of CPU resources in order to be able to guarantee timing constraints of its internal parts (tasks). In static systems, an exact amount of CPU resource can be allocated to a subsystem. However, in dynamic systems, where execution times of tasks vary considerably during run-time, it is desirable to give a dynamic portion of the CPU given the current load situation. In this thesis we present a feedback control approach for adapting the amount of CPU resource that is allocated to subsystems during run-time such that each subsystem receives sufficient resources while keeping the number of deadline violations to a minimum. We also show some example simulations where the controller adapts the budget of a subsystems.If we allocate CPU only based on subsystems demand and don't take into account the availability of the resource, timing guarantees of the lower priority subsystems (using a priority based scheduler in the global level) will be violated in the overload situations. In such a situation the high criticality modules should be superior to the low criticality modules in receiving resources. In this thesis, in the extension of our adaptive framework, we propose two techniques for controlling the CPU distribution among modules in an overload circumstance. First we introduce the notion of subsystem criticality and then distribute CPU portions based on the criticality level of subsystems.
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Wlassich, John J. (John James). "Nonlinear force feedback impedance control." Thesis, Massachusetts Institute of Technology, 1986. http://hdl.handle.net/1721.1/15032.

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Abdelrahim, Mahmoud. "Output feedback event-triggered control." Thesis, Université de Lorraine, 2014. http://www.theses.fr/2014LORR0110/document.

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La commande à transmissions événementielles est une approche dans laquelle les instants de transmission sont définis selon un critère dépendant de l'état du système et non plus d'une horloge à l'instar des implantations périodiques. Dans cette thèse, nous nous concentrons sur la synthèse de telles lois de commande par retour de sortie. Les contributions sont les suivantes : (i) nous proposons une méthode de synthèse dite par émulation pour des systèmes non linéaires; (ii) nous présentons une méthode de synthèse jointe de la loi de commande et de la condition de déclenchement pour les systèmes linéaires; (iii) nous nous intéressons au cas de systèmes non linéaires singulièrement perturbés et nous construisons le contrôleur à partir d’approximation de la dynamique lente uniquement
Event-triggered control is a sampling paradigm in which the sequence of transmission instants is determined based on the violation of a state-dependent criterion and not a time-driven clock. In this thesis, we deal with event-triggered output-based controllers to stabilize classes of nonlinear systems. The contributions of the presented material are threefold: (i) we stabilize a class of nonlinear systems by using an emulation-based approach; (ii) we develop a co-design procedure to simultaneously design the output feedback law and the event-triggering condition for linear systems; (iii) we propose stabilizing event-triggered controllers for nonlinear systems whose dynamics have two-time scales (in particular, we only rely on the knowledge of an approximate model of the slow dynamics)
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Yang, Yugu. "Feedback Control for Electron Beam Lithography." UKnowledge, 2012. http://uknowledge.uky.edu/ece_etds/9.

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Scanning-electron-beam lithography (SEBL) is the primary technology to generate arbitrary features at the nano-scale. However, pattern placement accuracy still remains poor compared to its resolution due to the open-loop nature of SEBL systems. Vibration, stray electromagnetic fields, deflection distortion and hysteresis, substrate charging, and other factors prevent the electron-beam from reaching its target position and one has no way to determine the actual beam position during patterning with conventional systems. To improve the pattern placement accuracy, spatial-phase-locked electron-beam lithography (SPLEBL) provides feedback control of electron-beam position by monitoring the secondary electron signal from electron-transparent fiducial grids on the substrate. While scanning the electron beam over the fiducial grids, the phase of the grid signal is analyzed to estimate the electron-beam position error; then the estimates are sent back to beam deflection system to correct the position error. In this way, closed-loop control is provided to ensure pattern placement accuracy. The implementation of spatial-phase-locking on high speed field-programmable gate array (FPGA) provides a low-cost method to create a nano-manufacturing platform with 1 nm precision and significantly improved throughput. Shot-to-shot, or pixel-to-pixel, dose variation during EBL is a significant practical and fundamental problem. Dose variations associated with charging, electron source instability, optical system drift, and ultimately shot noise in the beam itself conspire to increase critical dimension variability and line width roughness and to limit the throughput. It would be an important improvement to e-beam patterning technology if real-time feedback control of electron-dose were provided to improve pattern quality and throughput even beyond the shot noise limit. A novel approach is proposed in this document to achieve the real-time dose control based on the measurement of electron arrival at the sample to be patterned, rather than from the source or another point in the electron-optical system. A dose control algorithm, implementation on FPGA, and initial experiment results for the real-time feedback dose control on the e-beam patterning tool is also presented.
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Polston, James D. "DECENTRALIZED ADAPTIVE CONTROL FOR UNCERTAIN LINEAR SYSTEMS: TECHNIQUES WITH LOCAL FULL-STATE FEEDBACK OR LOCAL RELATIVE-DEGREE-ONE OUTPUT FEEDBACK." UKnowledge, 2013. http://uknowledge.uky.edu/me_etds/24.

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This thesis presents decentralized model reference adaptive control techniques for systems with full-state feedback and systems with output feedback. The controllers are strictly decentralized, that is, each local controller uses feedback from only local subsystems and no information is shared between local controllers. The full-state feedback decentralized controller is effective for multi-input systems, where the dynamics matrix and control-input matrix are unknown. The decentralized controller achieves asymptotic stabilization and command following in the presence of sinusoidal disturbances with known spectrum. We present a construction technique of the reference-model dynamics such that the decentralized controller is effective for systems with arbitrarily large subsystem interconnections. The output-feedback decentralized controller is effective for single-input single-output subsystems that are minimum phase and relative degree one. The decentralized controller achieves asymptotic stabilization and disturbance rejection in the presence of an unknown disturbance, which is generated by an unknown Lyapunov-stable linear system.
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Gedeon, Tomáš. "Cyclic feedback systems." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/29161.

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Vinnicombe, Glenn. "Measuring robustness of feedback systems." Thesis, University of Cambridge, 1992. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.281872.

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Books on the topic "Feedback control"

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Dodds, Stephen J. Feedback Control. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6675-7.

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M, Parr John, ed. Feedback control systems. 5th ed. Boston: Prentice Hall, 2011.

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1940-, Harbor Royce D., ed. Feedback control systems. 4th ed. London: Prentice Hall International, 2000.

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

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Abramovici, Alex, and Jake Chapsky. Feedback Control Systems. Boston, MA: Springer US, 2000. http://dx.doi.org/10.1007/978-1-4615-4345-9.

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A, Francis Bruce, and Tannenbaum Allen 1953-, eds. Feedback control theory. Mineola, N.Y: Dover, 2008.

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Feedback control systems. 3rd ed. London: Prentice-Hall International, 1994.

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M, Kirillova F., and Prischepova S. V. 1964-, eds. Optimal feedback control. London: Springer, 1995.

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L, Phillips Charles. Feedback control systems. Englewood Cliffs, N.J: Prentice Hall, 1988.

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Vegte, J. Vande. Feedback control systems. Englewood Cliffs, NJ: Prentice-Hall, 1986.

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Book chapters on the topic "Feedback control"

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Moir, Tom. "State-Space Control." In Feedback, 199–222. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34839-7_8.

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Moir, Tom. "Introduction to Feedback Control." In Feedback, 1–19. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34839-7_1.

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Moir, Tom. "Introduction to Optimal Control." In Feedback, 443–65. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34839-7_17.

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Wang, Qing-Guo, Tong Heng Lee, and Chong Lin. "Decentralized Control." In Relay Feedback, 347–73. London: Springer London, 2003. http://dx.doi.org/10.1007/978-1-4471-0041-6_12.

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Moir, Tom. "Speed and Position-Control Systems." In Feedback, 61–88. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-34839-7_4.

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Yang, Yi, and Furong Gao. "Feedback Control." In Computer Modeling for Injection Molding, 313–37. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013. http://dx.doi.org/10.1002/9781118444887.ch12.

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Hong, Keum-Shik, and Umer Hameed Shah. "Feedback Control." In Dynamics and Control of Industrial Cranes, 115–41. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-5770-1_7.

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Fleming, Andrew J., and Kam K. Leang. "Feedback Control." In Design, Modeling and Control of Nanopositioning Systems, 175–219. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-06617-2_7.

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Brogliato, Bernard. "Feedback control." In Communications and Control Engineering, 397–461. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0557-2_8.

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Gooch, Jan W. "Feedback Control." In Encyclopedic Dictionary of Polymers, 298. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4419-6247-8_4815.

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Conference papers on the topic "Feedback control"

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Li, Mu, Jian Sun, and Lihua Dou. "Stability analysis of dynamic quantized feedback system with packet loss." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334631.

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Holm, Mirjam, Pablo Ballesteros, Stephan Beitler, Alex Tarasow, and Christian Bohn. "Active control of speed fluctuations in rotating machines using feedback linearization." In 2012 UKACC International Conference on Control (CONTROL). IEEE, 2012. http://dx.doi.org/10.1109/control.2012.6334607.

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Wang, Chi-Lun, and Jia-Ying Tu. "Mixed H2/H∞ feedback control of multivariable dynamically substructured systems." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915134.

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da Rocha Pinto, Joao Miguel, Pierre Ricco, and George Papadakis. "Wall-transpiration feedback control of laminar streaks using an adjoint approach." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915141.

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Heins, Peter H., Bryn Ll Jones, and Ati S. Sharma. "Passivity-based feedback control of a channel flow for drag reduction." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915144.

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Hu, Junyan, and Alexander Lanzon. "Cooperative Control of Innovative Tri-Rotor Drones Using Robust Feedback Linearization." In 2018 UKACC 12th International Conference on Control (CONTROL). IEEE, 2018. http://dx.doi.org/10.1109/control.2018.8516820.

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Welch, Ashley J. "Reflectance feedback control." In Medical Optical Tomography: Functional Imaging and Monitoring, edited by Gerhard J. Mueller. SPIE, 1993. http://dx.doi.org/10.1117/12.2283759.

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Garg, Siddharth, Diana Marculescu, and Radu Marculescu. "Custom feedback control." In the 16th ACM/IEEE international symposium. New York, New York, USA: ACM Press, 2010. http://dx.doi.org/10.1145/1840845.1840939.

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Khalid, Nasir, and Attaullah Y. Memon. "Output feedback stabilization of an Inertia Wheel Pendulum using Sliding Mode Control." In 2014 UKACC International Conference on Control (CONTROL). IEEE, 2014. http://dx.doi.org/10.1109/control.2014.6915132.

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Saralegui, U., M. De la Sen, and S. Alonso-Quesada. "A feedback vaccination law for an SIR epidemic model: A case study." In 2016 UKACC 11th International Conference on Control (CONTROL). IEEE, 2016. http://dx.doi.org/10.1109/control.2016.7737565.

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Reports on the topic "Feedback control"

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Safonov, Michael G. Robust Control Feedback and Learning. Fort Belvoir, VA: Defense Technical Information Center, November 2002. http://dx.doi.org/10.21236/ada399708.

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White, R. B., P. H. Rutherford, H. P. Furth, W. Park, and L. Chen. Feedback control of resistive instabilities. Office of Scientific and Technical Information (OSTI), December 1985. http://dx.doi.org/10.2172/6294993.

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Zhang S. Y. and A. McNerney. RFQ AMPLITUDE FEEDBACK LOOP CONTROL. Office of Scientific and Technical Information (OSTI), April 1986. http://dx.doi.org/10.2172/1151162.

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Schwartz, C. Modeling transverse orbit feedback control. Office of Scientific and Technical Information (OSTI), January 2001. http://dx.doi.org/10.2172/774052.

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Sarker, Z., C. Perkins, V. Singh, and M. Ramalho. RTP Control Protocol (RTCP) Feedback for Congestion Control. RFC Editor, January 2021. http://dx.doi.org/10.17487/rfc8888.

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Maase, Hannon T., Jonathan A. Locker, and Philip T. Krein. Bus Current Feedback for Motor Control. Fort Belvoir, VA: Defense Technical Information Center, January 2000. http://dx.doi.org/10.21236/ada377502.

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Forbush, Dominic. Load Mitigating Feedback Control of WECs. Office of Scientific and Technical Information (OSTI), March 2022. http://dx.doi.org/10.2172/1854659.

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Collins Jr, Emmanuel G. Feedback Control Design for Counterflow Thrust Vectoring. Fort Belvoir, VA: Defense Technical Information Center, September 2005. http://dx.doi.org/10.21236/ada438337.

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Petkov, Petko, and Mihail Konstantinov. Perturbation Analysis of the Feedback Control Problem. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2018. http://dx.doi.org/10.7546/crabs.2018.02.12.

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Petkov, Petko. Perturbation Analysis of the Feedback Control Problem. "Prof. Marin Drinov" Publishing House of Bulgarian Academy of Sciences, February 2018. http://dx.doi.org/10.7546/grabs2018.2.12.

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