Modelling and effective controlling of radial electromagnetic bearings

Jarcuska, Martin; Zalman, Milan; Mudrakova, Tatiana; Varga, Pavol

doi:10.1109/carpathiancc.2014.6843597

Cited by 2 publications

(2 citation statements)

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“…With constant current dependence of position on the force is determined as deviation from the operating point. [5] A tangent is drawn to the magnetic force-position curve at the operating point is drawn as seen in The force-displacement factor describes the slope of the tangent line.…”

Section: Iiactive Magnetic Bearing Modelingmentioning

confidence: 99%

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Improvement of Operational Performance of Active Magnetic Bearing using Nonlinear LQG Controller

Janardhanan¹,

Mathew²

2015

IJERT

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Active Magnetic Bearing provides a means of supporting the body by magnetic forces without physical contact. Active magnetic bearings are inherently unstable systems due to the indirect proportionality between the attractive force and the length of the air gap. Thus, to stabilize the rotor position, a closed-loop control has to be implemented. The aim of this paper is to design a LQG controller and a nonlinear LQG controller to control the position of rotating shaft. First AMB is modeled using mathematical equations. LQG controller is a combination of Linear Quadratic Regulator (LQR) and Kalman Filter which is an optimal estimator.Extended Kalman Filter (EKF) is an extension of LQG which can be applied to nonlinear systems. It itself linearizes the model about an operating point. Using these controllers the magnetic bearing is capable to maintain the rotor position and in turn maintain the position of shaft connected to it.The performance of system with each controller is compared. This can be implemented using MATLAB/SIMULINK software.

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Section: Iiactive Magnetic Bearing Modelingmentioning

confidence: 99%

“…Availability of all states for control at a time is not realistic hence have to use an estimator for this purpose. It works based on recursion principle and thus it will find out the best estimate among the past present and future estimates [5].…”

Section: Kalman Filtermentioning

confidence: 99%