Control of magnetic bearing systems via the Chebyshev polynomial-based unified model (CPBUM) neural network

2000 ◽  
Vol 30 (1) ◽  
pp. 85-92 ◽  
Author(s):  
Jin-Tsong Jeng ◽  
Tsu-Tian Lee
Keyword(s):  
Neural Network ◽  
Chebyshev Polynomial ◽  
Magnetic Bearing ◽  
Unified Model

PIDNN control for Vernier-gimballing magnetically suspended flywheel under nonlinear change of stiffness and disturbance

2020 ◽  
pp. 095965182097757
Author(s):  
Jiqiang Tang ◽  
Mengyue Ning ◽  
Xu Cui ◽  
Tongkun Wei ◽  
Xiaofeng Zhao
Keyword(s):  
Neural Network ◽  
Attitude Control ◽  
Magnetic Force ◽  
Magnetic Bearing ◽  
Network Control ◽  
Gradient Descent Method ◽  
Neural Network Control ◽  
Proportional Integral Derivative ◽  
Tracking Accuracy ◽  
Proportional Integral

Vernier-gimballing magnetically suspended flywheel is often used for attitude control and interference suppression of spacecrafts. Due to the special structure of the conical magnetic bearing, the radial component generated by the axial magnetic force and the change of the magnetic air gap will cause the nonlinearity of stiffness and disturbance. That will lead to not only poor stability of the suspension control system but also unsatisfactory tracking accuracy of the rotor position. To solve the nonlinear problem of the system, this article proposes a proportional–integral–derivative neural network control scheme. First, the rotor model considering the nonlinear variation of disturbance and stiffness parameters is established. Then, the weight of neural network is adjusted by the gradient descent method online to ensure the accurate output of magnetic force. Finally, the convergence analysis is carried out based on the Lyapunov stability theory. Compared with the general proportional–integral–derivative control and the radial basis function neural network control, the simulation results demonstrate that the proposed method has the highest tracking accuracy and excellent performance in improving stability. The experimental results prove the correctness of the theoretical analysis and the validity of the proposed 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 Chebyshev polynomial feedforward neural network trained by differential evolution and its application in environmental case studies

2020 ◽  
Vol 126 ◽  
pp. 104663
Author(s):  
Ioannis A. Troumbis ◽  
George E. Tsekouras ◽  
John Tsimikas ◽  
Christos Kalloniatis ◽  
Dias Haralambopoulos
Keyword(s):  
Neural Network ◽  
Differential Evolution ◽  
Case Studies ◽  
Chebyshev Polynomial ◽  
Feedforward Neural Network

Neural Network Emulation of a Magnetically Suspended Rotor

2004 ◽  
Vol 126 (2) ◽  
pp. 373-384 ◽  
Author(s):  
A. Escalante ◽  
V. Guzma´n ◽  
M. Parada ◽  
L. Medina ◽  
S. E. Diaz
Keyword(s):  
Neural Network ◽  
Virtual Instrument ◽  
High Speed ◽  
Magnetic Bearing ◽  
Magnetic Bearings ◽  
System Response ◽  
The Neural Network ◽  
Artificial Neurons ◽  
Position Data ◽  
System 2

The use of magnetic bearings in high speed/low friction applications is increasing in industry. Magnetic bearings are sophisticated electromechanical systems, and modeling magnetic bearings using standard techniques is complex and time consuming. In this work a neural network is designed and trained to emulate the operation of a complete system (magnetic bearing, PID controller, and power amplifiers). The neural network is simulated and integrated into a virtual instrument that will be used in the laboratory both as a teaching and a research tool. The main aims in this work are: (1) determining the minimum amount of artificial neurons required in the neural network to emulate the magnetic bearing system, (2) determining the more appropriate ANN training method for this application, and (3) determining the errors produced when a neural network trained to emulate system operation with a balanced rotor is used to predict system response when operating with an unbalanced rotor. The neural network is trained using as input the position data from the proximity sensors; neural network outputs are the control signals to the coil amplifiers.

Modified single-output Chebyshev-polynomial feedforward neural network aided with subset method for classification of breast cancer

2019 ◽  
Vol 350 ◽  
pp. 128-135 ◽  
Author(s):  
Long Jin ◽  
Zhiguan Huang ◽  
Liangming Chen ◽  
Mei Liu ◽  
Yuhe Li ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Chebyshev Polynomial ◽  
Feedforward Neural Network ◽  
Single Output
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