scholarly journals Movement Control Method of Magnetic Levitation System Using Eccentricity of Non-Contact Position Sensor

2021 ◽  
Vol 11 (5) ◽  
pp. 2396
Author(s):  
Jong Suk Lim ◽  
Hyung-Woo Lee
Keyword(s):  
Motor System ◽  
Control Method ◽  
Magnetic Levitation ◽  
Movement Control ◽  
Position Sensor ◽  
Semiconductor Wafer ◽  
Control Command ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
Contact Position

This paper presents a method of utilizing a non-contact position sensor for the tilting and movement control of a rotor in a rotary magnetic levitation motor system. This system has been studied with the aim of having a relatively simple and highly clean alternative application compared to the spin coater used in the photoresist coating process in the semiconductor wafer process. To eliminate system wear and dust problems, a shaft-and-bearing-free magnetic levitation motor system was designed and a minimal non-contact position sensor was placed. An algorithm capable of preventing derailment and precise movement control by applying only control without additional mechanical devices to this magnetic levitation system was proposed. The proposed algorithm was verified through simulations and experiments, and the validity of the algorithm was verified by deriving a precision control result suitable for the movement control command in units of 0.1 mm at 50 rpm rotation drive.

Research on Fuzzy Control Method of Hybrid Maglev System

2014 ◽  
Vol 511-512 ◽  
pp. 1039-1043
Author(s):  
Qing Zhen Wang ◽  
A Ming Hao ◽  
Zhi Qiang Wang
Keyword(s):  
Fuzzy Control ◽  
Control Method ◽  
Magnetic Levitation ◽  
System Simulation ◽  
Dynamic Performance ◽  
Maglev System ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
Simulation Results ◽  
Steady State Performance

Combining with the principle of permanent magnetic and electromagnetic suspension, we can establish its mathematical model. Maglev system is a typical nonlinear system. Simulation model is built based on fuzzy control in matlab by combining the advantages of fuzzy control. By comparing the simulation results with PID, the simulation results show that fuzzy control can make the magnetic levitation system has better dynamic performance and steady-state performance.

scholarly journals Experiments with Neural Networks in the Identification and Control of a Magnetic Levitation System Using a Low-Cost Platform

2021 ◽  
Vol 11 (6) ◽  
pp. 2535
Author(s):  
Bruno E. Silva ◽  
Ramiro S. Barbosa
Keyword(s):  
Neural Networks ◽  
Neural Control ◽  
Magnetic Levitation ◽  
Low Cost ◽  
Training Data ◽  
Neural Controllers ◽  
Levitation System ◽  
Internal Model Controller ◽  
Magnetic Levitation System ◽  
And Performance

In this article, we designed and implemented neural controllers to control a nonlinear and unstable magnetic levitation system composed of an electromagnet and a magnetic disk. The objective was to evaluate the implementation and performance of neural control algorithms in a low-cost hardware. In a first phase, we designed two classical controllers with the objective to provide the training data for the neural controllers. After, we identified several neural models of the levitation system using Nonlinear AutoRegressive eXogenous (NARX)-type neural networks that were used to emulate the forward dynamics of the system. Finally, we designed and implemented three neural control structures: the inverse controller, the internal model controller, and the model reference controller for the control of the levitation system. The neural controllers were tested on a low-cost Arduino control platform through MATLAB/Simulink. The experimental results proved the good performance of the neural controllers.

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