Nonlinear control of a novel active magnetic bearings technology based high-precision positioning stage

Basovich, Sergei; Arogeti, Shai; Brand, Ziv; Levi, N.

doi:10.1109/med.2013.6608914

Cited by 4 publications

(3 citation statements)

References 11 publications

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“…LQR is the controller and it assumes full state information while Kalman Filter estimates states from output measurement. LQG control is based on Separation Principle Consider a linear system, ẋ= Ax + Bu y=Cx+Du (8) Design a control law u= such that the performance measure is minimised.…”

Section: Alinear Quadratic Guassian Controllermentioning

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: Alinear Quadratic Guassian Controllermentioning

confidence: 99%

“…The EKF uses a replica of the nonlinear plant, correcting the state-estimate by a linear gain matrix,H(t),multiplying the residual vector, r(t) The state estimate vector ̂ is determined by solving the non linear differential equations [8].…”

Section: B Extended Kalman Filtermentioning

confidence: 99%

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

Janardhanan¹,

Mathew²

2015

IJERT

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“…They also verified that genetic algorithm can improve the performance and stability of controller. Basovich et al (2013) achieved the control of a novel 6-DOF positioning stage using AMBs by proposing an output feedback nonlinear controller combined with velocity observers. In conclusion, nonlinear control methods work well in accuracy and responsiveness in condition of rotor large motion due to its nonlinearity.…”

Section: Introductionmentioning

confidence: 99%

Sliding mode control with deep learning method for rotor trajectory control of active magnetic bearing system

Yao

Chen

2018

Transactions of the Institute of Measurement and Control

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Active magnetic bearing (AMB) is competent in rotor trajectory control for potential applications such as mechanical processing and spindle attitude control, while the highly nonlinear and coupled dynamic characteristics especially in the condition of rotor large motion are obstacles in controller design. In this paper, a controller of AMB is proposed to achieve rotor 3D trajectory control. First, the dynamic model of the AMB-rotor system containing a nonlinear electromagnetic force model is introduced. Then the DCNN-SMC (deep convolutional neural network - sliding mode control) controller is proposed. Sliding mode control is used to achieve the tracking control with high robustness and responsiveness, and a deep convolutional neural network based on deep learning method is designed to compensate the uncertainties of the system. Finally, simulation of a 5-degree of freedom (DOF) system on various trajectories demonstrates evident control effect of the proposed controller in precision and significant effect of DCNN based on deep learning method in compensation control.

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Adaptive output zero-bias tracking control of 1DOF AMB suspension system

Basovich

Arogeti

Brand

2014

2014 13th International Conference on Control Automation Robotics &Amp; Vision (ICARCV)

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Nonlinear control of a novel active magnetic bearings technology based high-precision positioning stage

Cited by 4 publications

References 11 publications

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

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

Sliding mode control with deep learning method for rotor trajectory control of active magnetic bearing system

Adaptive output zero-bias tracking control of 1DOF AMB suspension system

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