scholarly journals Modeling of Electrodynamic Suspension Systems

2006 ◽  
Author(s):  
Nam Ho Kim ◽  
Long Ge
Keyword(s):  
Dynamic Models ◽  
Magnetic Levitation ◽  
Optimization Technique ◽  
Suspension Systems ◽  
Concentric Force ◽  
Numerical Tests ◽  
Contact Constraint ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
Electrodynamic Suspension

Characteristics of magnetic–levitation system are studied using dynamic models that include motion–dependent lift, drag, slip, and roll motions. In addition, the contact constraint between the vehicle and the track is modeled using the penalty method. Unknown numerical parameters are identified using the optimization technique. The numerical tests are focused on the damping characteristic, stability in lifting and slip motions, the lifting efficiency compared with the concentric force, and contact with track.

Nonlinear Control of a Magnetic Levitation System Using Single Multiplicative Neuron Models

2013 ◽  
Author(s):  
Daniel L. Hall ◽  
Biswanath Samanta
Keyword(s):  
Nonlinear Control ◽  
Feedback Linearization ◽  
Magnetic Levitation ◽  
Optimization Technique ◽  
Real Life ◽  
Population Based ◽  
Neuron Models ◽  
Levitation System ◽  
Magnetic Levitation System ◽  
The One

The paper presents an approach to nonlinear control of a magnetic levitation system using artificial neural networks (ANN). A novel form of ANN, namely, single multiplicative neuron (SMN) model is proposed in place of more traditional multi-layer perceptron (MLP). SMN derives its inspiration from the single neuron computation model in neuroscience. SMN model is trained off-line, to estimate the network weights and biases, using a population based stochastic optimization technique, namely, particle swarm optimization (PSO). Both off-line training and on-line learning of SMN have been considered. The ANN based techniques have been compared with a feedback linearization approach. The development of the control algorithms is illustrated through the hardware-in-the-loop (HIL) implementation of magnetic levitation in LabVIEW environment. The controllers based on ANN performed quite well and better than the one based on feedback linearization. However, the SMN structure was much simpler than the MLP for similar performance. The simple structure and faster computation of SMN have the potential to make it a preferred candidate for implementation of real-life complex magnetic levitation systems.

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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