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Related Experiment Video

Updated: Jun 23, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Robust dynamic sliding-mode control using adaptive RENN for magnetic levitation system.

Faa-Jeng Lin¹, Syuan-Yi Chen, Kuo-Kai Shyu

¹Department of Electrical Engineering, National Central University, Jhong-Li, Taoyuan 320, Taiwan. linfj@ee.ncu.edu.tw

IEEE Transactions on Neural Networks

|May 9, 2009

Summary

A robust dynamic sliding mode control system (RDSMC) using a recurrent Elman neural network (RENN) enhances magnetic levitation control. This system effectively manages uncertainties for improved performance and robustness in practical applications.

Related Experiment Videos

Last Updated: Jun 23, 2026

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

Area of Science:

Control Systems Engineering
Artificial Intelligence
Robotics

Background:

Magnetic levitation systems require precise control despite inherent uncertainties.
Traditional control methods like PID and Sliding Mode Control (SMC) face limitations with unknown dynamics and hardware constraints.
Dynamic Sliding Mode Control (DSMC) aims to mitigate chattering but struggles with unknown uncertainty bounds.

Purpose of the Study:

To propose a Robust Dynamic Sliding Mode Control system (RDSMC) for magnetic levitation systems.
To enhance control performance and robustness by addressing uncertainties.
To utilize a Recurrent Elman Neural Network (RENN) for online estimation of system uncertainties.

Main Methods:

Derivation of the magnetic levitation system's dynamic model.
Development of a PID-type DSMC to reduce chattering.
Integration of an RENN estimator within the RDSMC to approximate nonlinear uncertainties online.
Application of Lyapunov stability theorem for adaptive RENN parameter learning.
Design of a robust compensator to handle approximation errors and higher-order terms.

Main Results:

The proposed RDSMC effectively estimates and compensates for lumped uncertainties in the magnetic levitation system.
Experimental results demonstrate successful tracking of various periodic trajectories.
The RENN-based approach significantly improves control performance and robustness compared to conventional methods.
The system exhibits enhanced stability and reduced sensitivity to unmodeled dynamics.

Conclusions:

The developed RDSMC system offers a robust and effective solution for controlling magnetic levitation systems with unknown uncertainties.
The combination of RENN and DSMC provides a powerful framework for adaptive and stable control.
The findings validate the practical applicability of the proposed control strategy for magnetic levitation systems.