A New Method of Controlling Active Magnetic Bearing through Neural Network

2006 ◽  
Author(s):  
Roger Achkar ◽  
Chaiban Nasr ◽  
Jerome Miras ◽  
Ali Charara
Keyword(s):  
Neural Network ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
New Method

A new method of controlling active magnetic bearing through neural network

2006 ◽  
Author(s):  
Roger Achkar ◽  
Chaiban Nasr ◽  
Jerome De Miras ◽  
Ali Charara
Keyword(s):  
Neural Network ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
New Method

scholarly journals Nonlinear Control of an Active Magnetic Bearing System Achieved Using a Fuzzy Control with Radial Basis Function Neural Network

2014 ◽  
Vol 2014 ◽  
pp. 1-18 ◽  
Author(s):  
Seng-Chi Chen ◽  
Van-Sum Nguyen ◽  
Dinh-Kha Le ◽  
Nguyen Thi Hoai Nam
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Fuzzy Logic Controller ◽  
Magnetic Bearing ◽  
Operating Conditions ◽  
Active Magnetic Bearing ◽  
Highly Nonlinear ◽  
Radial Basis ◽  
Amb System

Studies on active magnetic bearing (AMB) systems are increasing in popularity and practical applications. Magnetic bearings cause less noise, friction, and vibration than the conventional mechanical bearings; however, the control of AMB systems requires further investigation. The magnetic force has a highly nonlinear relation to the control current and the air gap. This paper proposes an intelligent control method for positioning an AMB system that uses a neural fuzzy controller (NFC). The mathematical model of an AMB system comprises identification followed by collection of information from this system. A fuzzy logic controller (FLC), the parameters of which are adjusted using a radial basis function neural network (RBFNN), is applied to the unbalanced vibration in an AMB system. The AMB system exhibited a satisfactory control performance, with low overshoot, and produced improved transient and steady-state responses under various operating conditions. The NFC has been verified on a prototype AMB system. The proposed controller can be feasibly applied to AMB systems exposed to various external disturbances; demonstrating the effectiveness of the NFC with self-learning and self-improving capacities is proven.

A Field Balancing Technique Based on Virtual Trial-Weights Method for a Magnetically Levitated Flexible Rotor

2014 ◽  
Vol 136 (9) ◽  
Author(s):  
Yingguang Wang ◽  
Jiancheng Fang ◽  
Shiqiang Zheng
Keyword(s):  
High Efficiency ◽  
Notch Filter ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
New Method ◽  
Mode Shapes ◽  
Flexible Rotor ◽  
Magnetically Levitated ◽  
Balancing Efficiency ◽  
The Impact

For a magnetically levitated flexible rotor (MLFR), the amount of residual imbalance not only generates undesired vibrations, but also results in excessive bending, which may cause it hit to the auxiliary bearings. Thus, balancing below the critical speed is essential for the MLFR to prevent the impact. This paper proposes a balancing method of high precision and high efficiency, basing on virtual trial-weights. First, to reduce the computed error of rotor's mode shapes, a synchronous notch filter is inserted into the active magnetic bearing (AMB) controller, achieving a free support status. Then, AMBs provide the rotor with the synchronous electromagnetic forces (SEFs) to simulate the trial-weights. The SEFs with the initial angles varying from 0 deg to 360 deg in the rotational frame system result in continuous changes in the MLFR's deflection. Last, correction masses are calculated according to the changes. Compared to the trail-weights method, the new method needs not test-runs, which improves the balancing efficiency. Compared to the no trail-weights method, the new method does not require a precise model of the rotor-bearing system, which is difficult to acquire in the real system. Experiment results show that the novel method can reduce the residual imbalance effectively and accurately.

Adaptive Forced Balancing for Multivariable Systems

1995 ◽  
Vol 117 (4) ◽  
pp. 496-502 ◽  
Author(s):  
S. Beale ◽  
B. Shafai ◽  
P. LaRocca ◽  
E. Cusson
Keyword(s):  
Feedback Control ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
New Method ◽  
Multivariable Systems ◽  
Force Transmission ◽  
Frequency Tracking ◽  
Mass Unbalance ◽  
And Performance ◽  
Siso Systems

Active magnetic bearing (AMB) actuators support rotors without friction but require feedback control for stabilization and performance. Autobalancing compensation causes AMBs to spin a rotor about its inertial axis to eliminate synchronous force transmission from mass unbalance. Because mass unbalance constitutes a sinusoidal sensor disturbance within the bandwidth, conventional methods can either cause instability or fail to preserve desired bandwidth. We introduce a new method called adaptive forced balancing (AFB) which overcomes these problems. We consider AFB with a frequency tracking capability for SISO systems (i.e., single-end AMB suspensions) and show how to extend it for the MIMO case as applied to a double-end AMB suspension.

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

2018 ◽  
Vol 41 (5) ◽  
pp. 1383-1394 ◽  
Author(s):  
Xuan Yao ◽  
Zhaobo Chen
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sliding Mode Control ◽  
Convolutional Neural Network ◽  
Sliding Mode ◽  
Magnetic Bearing ◽  
Trajectory Control ◽  
Active Magnetic Bearing ◽  
Learning Method ◽  
Mode Control

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.

Intelligent Kalman Filtering for Fault Detection on an Active Magnetic Bearing System

2008 ◽  
Author(s):  
Nana K. Noel ◽  
Kari Tammi ◽  
Gregory D. Buckner ◽  
Nathan S. Gibson
Keyword(s):  
Neural Network ◽  
Kalman Filter ◽  
Fault Detection ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
Estimation Accuracy ◽  
False Alarms ◽  
Single Input Single Output ◽  
Single Output ◽  
Modeling Uncertainties

One of the challenges of condition monitoring and fault detection is to develop techniques that are sufficiently sensitive to faults without triggering false alarms. In this paper we develop and experimentally demonstrate an intelligent approach for detecting faults in a single-input, single-output active magnetic bearing. This technique uses an augmented linear model of the plant dynamics together with a Kalman filter to estimate fault states. A neural network is introduced to enhance the estimation accuracy and eliminate false alarms. This approach is validated experimentally for two types of fabricated faults: changes in suspended mass and coil resistance. The Kalman filter alone is shown to be incapable of identifying all fault cases due to modeling uncertainties. When an artificial neural network is trained to compensate for these uncertainties, however, all fault conditions are identified uniquely.

Research on Power Amplifier for AMB Based on DSP

2012 ◽  
Vol 150 ◽  
pp. 217-220
Author(s):  
Jian Sheng Zhang ◽  
Da Jun Jiang
Keyword(s):  
Neural Network ◽  
Control System ◽  
Power Amplifier ◽  
Electromagnetic Force ◽  
Design Method ◽  
Rbf Neural Network ◽  
Magnetic Bearing ◽  
Active Magnetic Bearing ◽  
System A ◽  
Neural Network Algorithm

Active Magnetic Bearing (AMB), which supports freely a rotor by using controllable electromagnetic force, have a lot of advantages such as adjustable stiffness and damp that the traditional bearings can’t compare with. Power Amplifier is an important part of AMB Control System. It can magnify or convert the control signal which can’t drive the electromagnetic iron actuators directly into a power signal. Hence, the performance of power amplifier plays a crucial role in the technique capability of control system. A design method of the PWM Power Amplifier was introduced through the research of the power Amplifier, and hence the CNC fault diagnoses are realized by RBF neural network algorithm and program.

A Neural Network-Based Closed Loop Identification of a Magnetic Bearings System

2004 ◽  
Author(s):  
Jose´ Medina ◽  
Mo´nica Parada ◽  
Victor Guzma´n ◽  
Luis Medina ◽  
Sergio Di´az
Keyword(s):  
Neural Network ◽  
Closed Loop ◽  
Magnetic Bearing ◽  
External Input ◽  
Open Loop ◽  
Active Magnetic Bearing ◽  
The Neural Network ◽  
Closed Loop Identification ◽  
Artificial Neural Network Ann ◽  
Amb System

This paper deals with the identification of a radial-type active magnetic bearing (AMB) system using Artificial Neural Network (ANN). Identification and validation experiments are performed on a laboratory magnetic bearing system. Since the electromechanical configuration is inherently unstable, the identification data is gathered while the AMB is operating in closed loop with a controller in the loop. From this data, the identification procedure generates an open-loop plant model. A NNARX (Neural network autoregressive external input model) structure is proposed and evaluated for emulating the system’s dynamic. The model is implemented by a Neural network, constructed using a multilayer perceptron (MLP) topology, and trained using as inputs the rotor’s displacements and excitation currents. Validation tests are performed under perturbation conditions (impact applied on the rotor). Results show that the neural network based model presented here is a powerful tool for dynamic plant’s identification, and that it could be also suitable for robust control application.

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