Performance comparisons of model-free control strategies for hybrid magnetic levitation system

2005 ◽  
Vol 152 (6) ◽  
pp. 1556 ◽  
Author(s):  
R.-J. Wai ◽  
J.-D. Lee
Keyword(s):  
Magnetic Levitation ◽  
Control Strategies ◽  
Model Free ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
Model Free Control ◽  
Performance Comparisons

scholarly journals Tracking Control of Magnetic Levitation System Using Model-Free RBF Neural Network Design

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 204563-204572
Author(s):  
Wang Yang ◽  
Fanwei Meng ◽  
Shengya Meng ◽  
Sun Man ◽  
Aiping Pang
Keyword(s):  
Neural Network ◽  
Network Design ◽  
Tracking Control ◽  
Magnetic Levitation ◽  
Rbf Neural Network ◽  
Model Free ◽  
Levitation System ◽  
Magnetic Levitation System

scholarly journals Toward Optimal Control of a Multivariable Magnetic Levitation System

2022 ◽  
Vol 12 (2) ◽  
pp. 674
Author(s):  
Paweł Majewski ◽  
Dawid Pawuś ◽  
Krzysztof Szurpicki ◽  
Wojciech P. Hunek
Keyword(s):  
Magnetic Levitation ◽  
Control Strategies ◽  
Control Process ◽  
Comparative Case Study ◽  
Feed Forward Neural Network ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
Control Procedures ◽  
And Control

In the paper, a comparative case study covering different control strategies of unstable and nonlinear magnetic levitation process is investigated. Three control procedures are examined in order to fulfill the specified performance indices. Thus, a dedicated PD regulator along with the hybrid fuzzy logic PID one as well as feed-forward neural network regulator are respected and summarized according to generally understood tuning techniques. It should be emphasized that the second PID controller is strictly derived from both arbitrary chosen membership functions and those ones selected through the genetic algorithm mechanism. Simulation examples have successfully confirmed the correctness of obtained results, especially in terms of entire control process quality of the magnetic levitation system. It has been observed that the artificial-intelligence-originated approaches have outperformed the classical one in the context of control accuracy and control speed properties in contrary to the energy-saving behavior whereby the conventional method has become a leader. The feature-related compromise, which has never been seen before, along with other crucial peculiarities, is effectively discussed within this paper.

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.

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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