Bibliographies thématiques / Magnetic Model Identification

Littérature scientifique sur le sujet « Magnetic Model Identification »

Auteur : Grafiati
Publié le 14 mars 2023

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Articles de revues sur le sujet "Magnetic Model Identification"

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Shabani, R., S. Tariverdilo et H. Salarieh. « Nonlinear identification of electro-magnetic force model ». Journal of Zhejiang University SCIENCE A 11, no 3 (12 février 2010) : 165–74. http://dx.doi.org/10.1631/jzus.a0900203.

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Va´zquez, J. A., E. H. Maslen, H. J. Ahn et D. C. Han. « Model Identification of a Rotor With Magnetic Bearings ». Journal of Engineering for Gas Turbines and Power 125, no 1 (27 décembre 2002) : 149–55. http://dx.doi.org/10.1115/1.1499730.

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The experimental identification of a long flexible rotor with three magnetic bearing journals is presented. Frequency response functions are measured between the magnetic bearing journals and the sensor locations while the rotor is suspended horizontally with piano wire. These frequency response functions are compared with the responses of a rotor model and a reconciliation process is used to reduce the discrepancies between the model and the measured data. In this identification, the wire and the fit of the magnetic bearing journals are identified as the sources of model error. As a result of the reconciliation process, equivalent dynamic stiffness are calculated for the piano wire and the fit of the magnetic bearing journals. Several significant numeral issues that were encountered during the process are discussed and solutions to some of these problems are presented.
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Lin, C. E., et H. L. Jou. « Force model identification for magnetic suspension systems via magnetic field measurement ». IEEE Transactions on Instrumentation and Measurement 42, no 3 (juin 1993) : 767–71. http://dx.doi.org/10.1109/19.231612.

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Rugkwamsook, P., et C. E. Korman. « Identification of magnetic aftereffect model parameters : Temperature dependence ». IEEE Transactions on Magnetics 34, no 4 (juillet 1998) : 1863–65. http://dx.doi.org/10.1109/20.706728.

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Armando, Eric, Radu Iustin Bojoi, Paolo Guglielmi, Gianmario Pellegrino et Michele Pastorelli. « Experimental Identification of the Magnetic Model of Synchronous Machines ». IEEE Transactions on Industry Applications 49, no 5 (septembre 2013) : 2116–25. http://dx.doi.org/10.1109/tia.2013.2258876.

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Pellegrino, Gianmario, Barbara Boazzo et Thomas M. Jahns. « Magnetic Model Self-Identification for PM Synchronous Machine Drives ». IEEE Transactions on Industry Applications 51, no 3 (mai 2015) : 2246–54. http://dx.doi.org/10.1109/tia.2014.2365627.

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Hall, Sebastian, Francisco J. Marquez-Fernandez et Mats Alakula. « Dynamic Magnetic Model Identification of Permanent Magnet Synchronous Machines ». IEEE Transactions on Energy Conversion 32, no 4 (décembre 2017) : 1367–75. http://dx.doi.org/10.1109/tec.2017.2704114.

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Ziolkowski, Marek, Hartmut Brauer et Milko Kuilekov. « Interface identification in magnetic fluid dynamics ». Serbian Journal of Electrical Engineering 1, no 1 (2003) : 61–69. http://dx.doi.org/10.2298/sjee0301061z.

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In magnetic fluid dynamics appears the problem of reconstruction of free boundary between conducting fluids, e.g. in aluminum electrolysis cells. We have investigated how the interface between two fluids of different conductivity of a highly simplified model of an aluminum electrolysis cell could be reconstructed by means of external magnetic field measurements using simple genetic algorithm.
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Li, Guoxin, Zongli Lin, Paul E. Allaire et Jihao Luo. « Modeling of a High Speed Rotor Test Rig With Active Magnetic Bearings ». Journal of Vibration and Acoustics 128, no 3 (2 décembre 2005) : 269–81. http://dx.doi.org/10.1115/1.2172254.

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This paper reports on the modeling and experimental identification of a high speed rotor-magnetic bearing test rig. An accurate nominal model and an uncertainty representation are developed for robust controller synthesis and analysis. A combination of analytical modeling, model updating, and identification is employed for each system component and for the system as a whole. This approach takes advantage of both the behavior modeling and input/output modeling methods. Analytical models of the rotor and the magnetic bearings are first developed from physical laws and refined by comparison with the experimental data. The substructure model is directly identified from the experimental data by a structured identification approach. Models of the electronic systems, such as the filters, amplifiers, sensors, and digital controller, are developed through experimental identification. These component models are then assembled to obtain the overall system model. Closed-loop tests are conducted to identify parameters in the model. Advanced control techniques based on H∞ control and μ synthesis are developed and successfully implemented on the test rig, which further validates the model.
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Mofidian, S. M. Mahdi, et Hamzeh Bardaweel. « Theoretical study and experimental identification of elastic-magnetic vibration isolation system ». Journal of Intelligent Material Systems and Structures 29, no 18 (10 juillet 2018) : 3550–61. http://dx.doi.org/10.1177/1045389x18783869.

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A vibration isolation system featuring a combination of elastic and magnetic springs and viscous and magnetic damping is presented. A mechanical flat spring houses a permanent magnet that is levitated between two stationary magnets. A prototype of the isolator is manufactured. COMSOL models are developed for the mechanical and magnetic springs. Measured data and model simulations show that the magnets arrangement results in nonlinear magnetic spring with negative linear stiffness. The mechanical spring exhibits linear behavior with positive stiffness. Experiments are performed and a nonlinear dynamic model is developed. The fabricated isolator is characterized at low and high acceleration levels. Results from model show good agreement with measured data at lower acceleration levels. Slight mismatch between model and experiment is evident at higher accelerations. This mismatch is due to the existence of lateral vibrations that are not accounted for in the unidirectional model. Results show that the combination of mechanical flat spring and magnetic spring reduces the resonant frequency of the isolator. In addition, results confirm the ability of the isolator to attenuate vibrations higher than 11.91 Hz when excited at 2.4525 [m s−2].
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Thèses sur le sujet "Magnetic Model Identification"

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Wroblewski, Adam C. « Model Identification, Updating, and Validation of an Active Magnetic Bearing High-Speed Machining Spindle for Precision Machining Operation ». Cleveland State University / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=csu1318379242.

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Mendes, Tiago. « Identification of the modulators of and the molecular pathways involved in the BIN1-Tau interaction ». Thesis, Lille 2, 2018. http://www.theses.fr/2018LIL2S033/document.

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Les principales caractéristiques neuropathologiques de la maladie d’Alzheimer (MA) sont les plaques séniles extracellulaires composées de peptide amyloïde β (Aβ) et les enchevêtrements neurofibrillaires intracellulaires composés de Tau hyperphosphorylé. Les mécanismes conduisant à la formation de ces lésions sont encore peu connus et le laboratoire a récemment caractériser le gène “bridging integrator 1” (BIN1), deuxième facteur de risque génétique le plus associé au risque de MA, comme facteur de risque potentiellement associé à la pathologie Tau. Une interaction entre les deux protéines a été décrite in vitro et in vivo suggérant que BIN1 pourrait être impliqué dans le développement de la pathologie associée à Tau dans le cadre de la MA. Cependant, ce rôle de l'interaction BIN1-Tau dans le processus pathophysiologique de la MA n'est pas connu et il reste ainsi à déterminer si cette interaction constitue une cible thérapeutique potentielle. Ce projet a visé alors à mieux comprendre les acteurs de cette interaction en identifiant les modulateurs et les voies moléculaires impliquées dans le contrôle de l'interaction BIN1-Tau, puis de déterminer comment cette interaction est modulée dans le contexte de la MA. Nous avons utilisé pour cela des approches complémentaires de biochimie, de résonance magnétique nucléaire et de microscopie confocale. Comme modèle cellulaire, des cultures primaires de neurones de rat ont été utilisées, et la méthode “proximity ligation assay” (PLA) a été développée comme approche principale pour observer l'interaction BIN1-Tau dans ces cellules. Nous avons déterminé que l'interaction se produit entre les domaines SH3 de BIN1 et le PRD de Tau et nous avons démontré que l’interaction est modulée par la phosphorylation de Tau et BIN1: la phosphorylation de la Thréonine 231 de Tau diminue son interaction avec BIN1, tandis que la phosphorylation de BIN1 à la Thréonine 348 (T348) augmente son interaction avec Tau. Nous avons mis au point une approche de criblage d’haut contenu semi-automatisée et basé sur une bibliothèque de composés commerciaux. Ce criblage s’est basé sur des cultures primaires de neurones comme modèle cellulaire et le PLA pour détecter l'interaction BIN1-Tau. Nous avons identifié plusieurs composés capables de moduler l'interaction BIN1-Tau, notamment U0126, un inhibiteur de MEK-1/2, qui diminue cette interaction, et la cyclosporine A, un inhibiteur de la calcineurine, qui au contraire augmente celle-ci en augmentant la phosphorylation de T348 de BIN1. Par ailleurs les “Cyclin-dependent kinases” (CDK) ont été montré comme contrôlant aussi ce site de phosphorylation. Nous avons donc mis en évidence le couple Calcineurine/CDK comme contrôlant la phosphorylation T348 de Bin1 et donc l’interaction BIN1-Tau. Nous avons également développé un modèle murin de tauopathie dans lequel nous avons surexprimé BIN1 humain. Nous avons observé que la surexpression de BIN1 résorbait les déficits de mémoire à long terme et réduisait la présence d'inclusions intracellulaires de Tau phosphorylée, provoquées par la surexpression de Tau, ce qui était associé à une augmentation de l'interaction BIN1-Tau. En utilisant des échantillons de cerveau humain post-mortem, nous avons observé que les niveaux de l’isoforme BIN1 neuronal étaient diminués dans les cerveaux d’AD, alors que les niveaux relatifs de BIN1 phosphorylé à T348 étaient augmentés, suggérant un mécanisme compensatoire. Cette étude a démontré la complexité et la dynamique de l’interaction BIN1-Tau dans les neurones, a révélé des modulateurs et des voies moléculaires potentiellement impliquées dans cette interaction, et a montré que les variations de l’expression ou de l’activité de BIN1 ont des effets directs sur l’apprentissage et la mémoire, possiblement liés à la régulation de son interaction avec Tau
The main neuropathological hallmarks of Alzheimer’s disease (AD) are the extracellular senile plaques composed of amyloid-β peptide (Aβ) and the intracellular neurofibrillary tangles composed of hyperphosphorylated Tau. The mechanisms leading to the formation of these lesions is not well understood and our lab has recently characterized the bridging integrator 1 (BIN1) gene, the second most associated genetic risk factor of AD and the first genetic risk factor to have a potential link to Tau pathology. The interaction between BIN1 and Tau proteins has been described in vitro and in vivo, which suggests that BIN1 might help us to understand Tau pathology in the context of AD. However, the role of BIN1-Tau interaction in the pathophysiological process of AD is not known, and whether this interaction is a potential therapeutic target remains to be determined. The aim of this project is to better understand the actors of BIN1-Tau interaction through the identification of the modulators and the molecular pathways involved therein, as well as to understand how BIN1-Tau interaction is modulated in the context of AD. We employed biochemistry, nuclear magnetic resonance, and confocal microscopy. We used rat primary neuronal cultures (PNC) as the cellular model and developed the proximity ligation assay (PLA) as the main readout of the BIN1-Tau interaction in cultured neurons. We determined that the interaction occurs between BIN1’s SH3 domain and Tau’s PRD domain, and demonstrated that it is modulated by Tau and BIN1 phosphorylation: phosphorylation of Tau at Threonine 231 decreases its interaction with BIN1, while phosphorylation of BIN1 at Threonine 348 (T348) increases its interaction with Tau. We developed a novel, semi-automated high content screening (HCS) assay based on a commercial compound library, also using PNC as the cellular model and PLA as the readout of BIN1-Tau interaction. We identified several compounds that are able to modulate the BIN1-Tau interaction, most notably U0126, an inhibitor of MEK-1/2, which reduced the interaction, and Cyclosporin A, an inhibitor of Calcineurin, which increased the interaction through increasing the BIN1 phosphorylation at T348. Furthermore, Cyclin-dependent kinases (CDK) were also shown as regulator of this phosphorylation site. These results suggest that the couple Calcineurin/CDK regulates BIN1 phosphorylation at T348 and consequently the BIN1-Tau interaction. We also developed a mouse model of tauopathy in which we overexpressed human BIN1. We observed that the overexpression of BIN1 rescued the long-term memory deficits and reduced the presence of intracellular inclusions of phosphorylated Tau, caused by Tau overexpression, and this was associated with an increase of BIN1-Tau interaction. Also, using post-mortem human brain samples, we observed that the levels of the neuronal BIN1 isoform were decreased in AD brains, whereas the relative levels of BIN1 phosphorylated at T348 were increased, suggesting a compensatory mechanism. Altogether, this study demonstrated the complexity and the dynamics of BIN1-Tau interaction in neurons, revealed modulators of and molecular pathways potentially involved in this interaction, and showed that variations in BIN1 expression or activity have direct effects on learning and memory, possibly linked to the regulation of its interaction with Tau
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Leplus, François. « Sur la modélisation numérique des transformateurs monophasé et triphasé : Application aux montages redresseurs et gradateurs ». Lille 1, 1989. http://www.theses.fr/1989LIL10073.

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Méthode de résolution des équations du système modélisé, indépendante de son environnement électrique, donnant un programme modulaire, utilisable dans un ensemble plus complexe. Extension de la méthode et procédure d'identification des paramètres. Expérimentation en fonctionnement redresseur et gradateur
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Olofsson, K. Erik J. « Nonaxisymmetric experimental modal analysis and control of resistive wall MHD in RFPs : System identification and feedback control for the reversed-field pinch ». Doctoral thesis, KTH, Fusionsplasmafysik, 2012. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-94096.

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The reversed-field pinch (RFP) is a device for magnetic confinement of fusion plasmas. The main objective of fusion plasma research is to realise cost-effective thermonuclear fusion power plants. The RFP is highly unstable as can be explained by the theory of magnetohydrodynamics (MHD). Feed-back control technology appears to enable a robustly stable RFP operation.  Experimental control and identification of nonaxisymmetric multimode MHD is pursued in this thesis. It is shown that nonparametric multivariate identification methods can be utilised to estimate MHD spectral characteristics from plant-friendly closed-loop operational input-output data. It is also shown that accurate tracking of the radial magnetic field boundary condition is experimentally possible in the RFP. These results appear generically useful as tools in both control and physics research in magnetic confinement fusion.

QC 20120508

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Livres sur le sujet "Magnetic Model Identification"

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Horing, Norman J. Morgenstern. Superfluidity and Superconductivity. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198791942.003.0013.

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Chapter 13 addresses Bose condensation in superfluids (and superconductors), which involves the field operator ψ‎ having a c-number component (<ψ(x,t)>≠0), challenging number conservation. The nonlinear Gross-Pitaevskii equation is derived for this condensate wave function<ψ>=ψ−ψ˜, facilitating identification of the coherence length and the core region of vortex motion. The noncondensate Green’s function G˜1(1,1′)=−i<(ψ˜(1)ψ˜+(1′))+> and the nonvanishing anomalous correlation function F˜∗(2,1′)=−i<(ψ˜+(2)ψ˜+(1′))+> describe the dynamics and elementary excitations of the non-condensate states and are discussed in conjunction with Landau’s criterion for viscosity. Associated concepts of off-diagonal long-range order and the interpretation of <ψ> as a superfluid order parameter are also introduced. Anderson’s Bose-condensed state, as a phase-coherent wave packet superposition of number states, resolves issues of number conservation. Superconductivity involves bound Cooper pairs of electrons capable of Bose condensation and superfluid behavior. Correspondingly, the two-particle Green’s function has a term involving a product of anomalous bound-Cooper-pair condensate wave functions of the type F(1,2)=−i<(ψ(1)ψ(2))+>≠0, such that G2(1,2;1′,2′)=F(1,2)F+(1′,2′)+G˜2(1,2;1′,2′). Here, G˜2 describes the dynamics/excitations of the non-superfluid-condensate states, while nonvanishing F,F+ represent a phase-coherent wave packet superposition of Cooper-pair number states and off-diagonal long range order. Employing this form of G2 in the G1-equation couples the condensed state with the non-condensate excitations. Taken jointly with the dynamical equation for F(1,2), this leads to the Gorkov equations, encompassing the Bardeen–Cooper–Schrieffer (BCS) energy gap, critical temperature, and Bogoliubov-de Gennes eigenfunction Bogoliubons. Superconductor thermodynamics and critical magnetic field are discussed. For a weak magnetic field, the Gorkov-equations lead to Ginzburg–Landau theory and a nonlinear Schrödinger-like equation for the pair wave function and the associated supercurrent, along with identification of the Cooper pair density. Furthermore, Chapter 13 addresses the apparent lack of gauge invariance of London theory with an elegant variational analysis involving re-gauging the potentials, yielding a manifestly gauge invariant generalization of the London equation. Consistency with the equation of continuity implies the existence of Anderson’s acoustic normal mode, which is supplanted by the plasmon for Coulomb interaction. Type II superconductors and the penetration (and interaction) of quantized magnetic flux lines are also discussed. Finally, Chapter 13 addresses Josephson tunneling between superconductors.
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Chapitres de livres sur le sujet "Magnetic Model Identification"

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Honc, Daniel. « Modelling and Identification of Magnetic Levitation Model CE 152/Revised ». Dans Advances in Intelligent Systems and Computing, 35–43. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91192-2_4.

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Czerwiński, Kamil, et Maciej Ławryńczuk. « Identification of Discrete-Time Model of Active Magnetic Levitation System ». Dans Advances in Intelligent Systems and Computing, 599–608. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-60699-6_58.

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Szewczyk, Roman. « Two Step, Differential Evolution-Based Identification of Parameters of Jiles-Atherton Model of Magnetic Hysteresis Loops ». Dans Advances in Intelligent Systems and Computing, 635–41. Cham : Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-77179-3_60.

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Alaggio, R., F. Benedettini, F. D’Innocenzo, G. D’Ovidio, D. Sebastiani et D. Zulli. « Modal Identification of Superconducting Magnetic Levitating Bogie ». Dans Conference Proceedings of the Society for Experimental Mechanics Series, 227–36. Cham : Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-15248-6_24.

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Zhou, Yujian, Jinhua She, Wangyong He, Danyun Li, Zhentao Liu et Yonghua Xiong. « On-Line Identification of Moment of Inertia for Permanent Magnet Synchronous Motor Based on Model Reference Adaptive System ». Dans Intelligent Robotics and Applications, 492–98. Cham : Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27535-8_44.

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Martynenko, Gennadii, et Volodymyr Martynenko. « Identification of Computational Models of the Dynamics of Gas Turbine Unit Rotors with Magnetic Bearings by Incomplete Data for Design Automation ». Dans Lecture Notes in Networks and Systems, 451–63. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-66717-7_38.

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Janghel, Rekh Ram. « Deep-Learning-Based Classification and Diagnosis of Alzheimer's Disease ». Dans Feature Dimension Reduction for Content-Based Image Identification, 193–217. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5775-3.ch011.

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Alzheimer's is the most common form of dementia in India and it is one of the leading causes of death in the world. Currently it is diagnosed by calculating the MSME score and by manual study of MRI scan. In this chapter, the authors develop and compare different methods to diagnose and predict Alzheimer's disease by processing structural magnetic resonance image scans (MRI scans) with deep learning neural networks. The authors implement one model of deep-learning networks which are convolution neural network (CNN). They use four different architectures of CNN, namely Lenet-5, AlexNet, ZFNet, and R-CNN architecture. The best accuracies for 75-25 cross validation and 90-10 cross validation are 97.68% and 98.75%, respectively, and achieved by ZFNet architecture of convolution neural network. This research will help in further studies on improving the accuracy of Alzheimer's diagnosis and prediction using neural networks.
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Chong, Shin-Horng, Roong-Soon Allan Chan et Norhaslinda Hasim. « Enhanced Nonlinear PID Controller for Positioning Control of Maglev System ». Dans Control Based on PID Framework - The Mutual Promotion of Control and Identification for Complex Systems. IntechOpen, 2021. http://dx.doi.org/10.5772/intechopen.96769.

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Magnetic levitation (maglev) is a way of using electromagnetic fields to levitate objects without any noise or the need for petrol or air. Due to its highly nonlinear and unstable behavior, numerous control solutions have been proposed to overcome it. However, most of them still acquire precise dynamic model parameters, or deep understanding of control theory. To account the complexity in the design procedure, a practical controller consists of classical and modern control approaches are proposed. This chapter presents a practical controller for high positioning performance of a magnetic levitation system. Three strategies of the proposed controller where the PI-PD controller is to enhance transient response, the model-based feedforward control (FF) is incorporated with the PI-PD controller to enhance the overshoot reduction characteristic in attaining a better transient response, and lastly the disturbance compensator (Kz) is integrated as an additional feedback element to reduce the sensitivity function magnitude for robustness enhancement. The proposed controller - FF PI-PD + Kz has a simple and straightforward design procedure. The usefulness of the proposed controller is evaluated experimentally.
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« Identification of strain energy function for magneto elastomer from pseudo pure shear test under the variance of magnetic field ». Dans Constitutive Models for Rubber VI, 475–80. CRC Press, 2009. http://dx.doi.org/10.1201/noe0415563277-89.

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Tsumori, F., H. Kotera et S. Ishikawa. « Identification of strain energy function for magneto elastomer from pseudo pure shear test under the variance of magnetic field ». Dans Constitutive Models for Rubber VI, 459–64. CRC Press, 2009. http://dx.doi.org/10.1201/noe0415563277.ch75.

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Actes de conférences sur le sujet "Magnetic Model Identification"

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Vázquez, José A., Eric H. Maslen, Hyeong-Joon Ahn et Dong-Chul Han. « Model Identification of a Rotor With Magnetic Bearings ». Dans ASME Turbo Expo 2001 : Power for Land, Sea, and Air. American Society of Mechanical Engineers, 2001. http://dx.doi.org/10.1115/2001-gt-0566.

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The experimental identification of a long flexible rotor with three magnetic bearing journals is presented. Frequency response functions (FRFs) are measured between the magnetic bearing journals and the sensor locations while the rotor is suspended horizontally with piano wire. These FRFs are compared with the responses of a rotor model and a reconciliation process is used to reduce the discrepancies between the model and the measured data. In this identification, the wire and the fit of the magnetic bearing journals are identified as the sources of model error. As a result of the reconciliation process, equivalent dynamic stiffness are calculated for the piano wire and the fit of the magnetic bearing journals. Several significant numeral issues that were encountered during the process are discussed and solutions to some of these problems are presented.
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Pratt, Richard L., et Andrew J. Petruska. « Magnetic Needle Steering Model Identification Using Expectation-Maximization ». Dans 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2019. http://dx.doi.org/10.1109/iros40897.2019.8968001.

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Yu, Z. C., D. Wen et H. Y. Zhang. « The Identification Model of Magnetic Bearing Supporting System ». Dans 2008 International Conference on Computer Science and Software Engineering. IEEE, 2008. http://dx.doi.org/10.1109/csse.2008.1324.

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Zhang, Maoqing, Zhongcheng Yu, Haini Qu et Yong Sun. « The DTNN Identification Model of Magnetic Bearing Supporting System ». Dans Proceedings of the International Conference. World Scientific Publishing Company, 2008. http://dx.doi.org/10.1142/9789812799524_0349.

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Pellegrino, Gianmario, Barbara Boazzo et Thomas M. Jahns. « Magnetic Model Self-Identification for PM Synchronous machine drives ». Dans 2014 International Conference on Optimization of Electrical and Electronic Equipment (OPTIM). IEEE, 2014. http://dx.doi.org/10.1109/optim.2014.6850934.

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Ortombina, L., D. Pasqualotto, F. Tinazzi et M. Zigliotto. « Magnetic Model Identification for Synchronous Reluctance Motors Including Transients ». Dans 2019 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE, 2019. http://dx.doi.org/10.1109/ecce.2019.8913164.

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Conway, R., S. Felix et R. Horowitz. « Parametric Uncertainty Identification and Model Reduction for Dual-Stage Robust H2 Track-following Control Synthesis ». Dans 2006 Asia-Pacific Magnetic Recording Conference. IEEE, 2006. http://dx.doi.org/10.1109/apmrc.2006.365907.

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Sun, Zhe, Jingjing Zhao, Zhengang Shi et Suyuan Yu. « Identification of Flexible Rotor Suspended by Magnetic Bearings ». Dans 2013 21st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/icone21-16220.

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Résumé :
Magnetic bearings are widely applied in High Temperature Gas-cooled Reactor (HTGR), where the rotating machineries are running under high purely helium environment. In designing and adjusting a magnetic bearing system, the mathematical model of the rotor plays an important role. Identification is a useful method to obtain the model of a rotor. However, there are some practical difficulties of identifying a magnetic bearing-rotor system without force sensors. This paper proposes an identification method for flexible rotor suspended by magnetic bearings. In this method, two experiments under different bearing stiffness are performed, the models obtained by these two experiments are then transformed to the desired rotor model and the influence of bearing stiffness is eliminated in this transformation. The proposed method is validated on an experimental system with a five degree-of-freedom suspended flexible rotor.
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Miranda, Jherson A., et Edgar A. Manzano. « Parametric Identification of an Active Magnetic Bearing System Using NARMAX Model ». Dans 2020 IEEE XXVII International Conference on Electronics, Electrical Engineering and Computing (INTERCON). IEEE, 2020. http://dx.doi.org/10.1109/intercon50315.2020.9220203.

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Armando, E., R. Bojoi, P. Guglielmi, G. Pellegrino et M. Pastorelli. « Experimental methods for synchronous machines evaluation by an accurate magnetic model identification ». Dans 2011 IEEE Energy Conversion Congress and Exposition (ECCE). IEEE, 2011. http://dx.doi.org/10.1109/ecce.2011.6063994.

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