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Magnetic Damping01:17

Magnetic Damping

451
Eddy currents can produce significant drag on motion, called magnetic damping. For instance, when a metallic pendulum bob swings between the poles of a strong magnet, significant drag acts on the bob as it enters and leaves the field, quickly damping the motion.
If, however, the bob is a slotted metal plate, the magnet produces a much smaller effect. When a slotted metal plate enters the field, an emf is induced by the change in flux; however, it is less effective because the slots limit the...
451
Magnetic Force01:18

Magnetic Force

955
In addition to the electric forces between electric charges, moving electric charges exert magnetic forces on each other. A magnetic field is created by a moving charge or a group of moving charges known as the electric current. A magnetic force is experienced by a second current or moving charge in response to this magnetic field. Fundamentally, interactions between moving electrons in the atoms of two bodies produce magnetic forces between them.
The magnetic force acting on a moving charge...
955
Magnetic Vector Potential01:15

Magnetic Vector Potential

616
In electrostatics, the electric field can be written as the negative gradient of the potential. In magnetostatics, the zero divergence of the magnetic field ensures that the magnetic field can be expressed as the curl of a vector potential. This potential is known as the magnetic vector potential.
Consider an ideal solenoid with n turns per unit length and radius R. If I is the current through the solenoid, the magnetic field inside the solenoid is expressed as the product of vacuum...
616
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

81
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
81
One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

487
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
487
Potential Due to a Magnetized Object01:24

Potential Due to a Magnetized Object

282
Magnetic dipoles in magnetic materials are aligned when placed under an external magnetic field. For paramagnets and ferromagnets, dipole alignment occurs in the direction of the magnetic field. However, the dipoles align opposite to the field in the case of diamagnets. This state of magnetic polarization due to the external field is called magnetization. Magnetization is defined as the dipole moment per unit volume. It plays a similar role to polarization in electrostatics.
The vector...
282

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Related Experiment Video

Updated: Jun 28, 2025

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics
07:42

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics

Published on: February 19, 2017

8.8K

Deep learning based model predictive controller on a magnetic levitation ball system.

Tianbo Peng1, Hui Peng1, Rongwei Li2

  • 1School of Automation, Central South University, Changsha, Hunan 410083, China; Xiangjiang Laboratory, Changsha 410205, China.

ISA Transactions
|April 21, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Long Short-Term Memory (LSTM) based auto-regressive model with exogenous input variables (LSTM-ARX) for controlling unstable magnetic levitation (maglev) systems. The proposed LSTM-ARX-based Predictive Functional Controller (PFC) significantly improves real-time control performance and efficiency.

Keywords:
LSTM-ARXMPCMagnetic levitation systemNonlinear modelPFC

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Area of Science:

  • Control Systems Engineering
  • Artificial Intelligence
  • Nonlinear Dynamics

Background:

  • Magnetic levitation (maglev) systems are crucial industrial devices known for nonlinearity and open-loop instability.
  • Accurate modeling of these systems is essential for effective control design.

Purpose of the Study:

  • To develop a precise pseudo-linear model for the maglev ball system using deep learning.
  • To design an efficient model predictive controller (MPC) and a predictive functional controller (PFC) for real-time maglev control.

Main Methods:

  • A Long Short-Term Memory (LSTM) network was modified into an auto-regressive model with exogenous input variables (LSTM-ARX) to deduce a locally linearized model.
  • The LSTM-ARX model was converted into a linear parameter varying (LPV) state-space model.
  • A model predictive controller (MPC) was derived, linearized for quadratic programming, and enhanced with a predictive functional controller (PFC) to reduce online optimization complexity.

Main Results:

  • The proposed LSTM-ARX model accurately describes the maglev ball system dynamics.
  • The LSTM-ARX-based Predictive Functional Controller (LSTM-ARX-PFC) was successfully implemented for real-time control.
  • Both simulation and experimental results demonstrated improved transient performance and efficiency.

Conclusions:

  • The LSTM-ARX-PFC offers a robust and efficient solution for controlling nonlinear, unstable systems like maglev.
  • Deep learning-based modeling combined with advanced control strategies provides significant advantages in system performance.