An optimal proportional integral derivative tuning for a magnetic levitation system using metamodeling approach
A magnetic levitation system (MLS) is a complex nonlinear system that requires an electromagnetic force to levitate an object in the air. The electromagnetic field is extremely sensitive to noise which can cause the acceleration on the spherical object, leading it to move into the unbalanced...
Saved in:
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
2022
|
Subjects: | |
Online Access: | http://eprints.uthm.edu.my/6628/1/J13913_e96684b53c0523eb123efa3f74441ec7.pdf http://eprints.uthm.edu.my/6628/ https://doi.org/ 10.11591/ijeecs.v25.i3.pp1356-1366 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Summary: | A magnetic levitation system (MLS) is a complex nonlinear system that
requires an electromagnetic force to levitate an object in the air. The
electromagnetic field is extremely sensitive to noise which can cause the
acceleration on the spherical object, leading it to move into the unbalanced
region. This paper presents a comparative assessment of controllers for the
magnetic levitation system using proportional integral derivative (PID)
controller based optimal tuning. The analysis was started by deriving the
mathematical model followed by the implementation of radial basis function
neural network (RBFNN) based metamodel. The optimal tuning of the PID
controller has offered better transient responses with the improvement of
overshoot and the rise time as compared to the standard optimization
methods. It is more robust and tolerant as compared to gradient descent
method. The simulation output using the radial basis based metamodel
approach showed an overshoot of 9.34% and rise time of 9.84 ms, which are
better than the gradient descent (GD) and conventional PID methods. For the
verification purpose, a Simscape model has been developed which mimic the
real model. It was found that the model has produced about similar
performance as what has been obtained from the Matlab simulation. |
---|