Neural Networks Control of a Magnetic Levitation System

2001 ◽  
Author(s):  
Chaiban Nasr
2021 ◽  
Vol 11 (6) ◽  
pp. 2535
Author(s):  
Bruno E. Silva ◽  
Ramiro S. Barbosa

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.


2017 ◽  
Vol 227 ◽  
pp. 113-121 ◽  
Author(s):  
José de Jesús Rubio ◽  
Lixian Zhang ◽  
Edwin Lughofer ◽  
Panuncio Cruz ◽  
Ahmed Alsaedi ◽  
...  

Author(s):  
G.M.K.B. Karunasena ◽  
H.D.N.S. Priyankara ◽  
B.G.D.A. Madhusank

This research investigates the acceptability of the Artificial Neural Networks (ANN) over the PID Controller for the control of the Magnetic Levitation System (MLS). In the field of advanced control systems, this system identifies as a feedback-controlled, single input- single output (SISO) system. This SISO system used a PID controller for vertical trajectory controlling of a metal sphere in airspace by using an electromagnetic force that directed to upward. The vertical position of the metal sphere controlled according to the applied magnetic force generated by a powerful electromagnet and the electromagnetic force controlled by varying the supply voltage. To control this nonlinear system, we develop a multilayer artificial neural network by using Matlab software and integrate that with the physical magnetic levitation model. According to specific initial conditions, the actual responses of the magnetic levitation system with artificial neural network compares the desire response of the metal sphere. The ability of control this nonlinear system by using neural networks validate by comparing results obtained by the PID controller and artificial neural network.


2006 ◽  
Vol 49 (4) ◽  
pp. 1073-1083 ◽  
Author(s):  
Agus TRISANTO ◽  
Muhammad YASSER ◽  
Ayman HAGGAG ◽  
Jianming LU ◽  
Takashi YAHAGI

Sign in / Sign up

Export Citation Format

Share Document