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

Faa Jeng Lin*, Syuan Yi Chen, Kuo Kai Shyu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

105 Citations (Scopus)


In this paper, a robust dynamic sliding mode control system (RDSMC) using a recurrent Elman neural network (RENN) is proposed to control the position of a levitated object of a magnetic levitation system considering the uncertainties. First, a dynamic model of the magnetic levitation system is derived. Then, a proportional-integral-derivative (PID)-type sliding-mode control system (SMC) is adopted for tracking of the reference trajectories. Moreover, a new PID-type dynamic sliding-mode control system (DSMC) is proposed to reduce the chattering phenomenon. However, due to the hardware being limited and the uncertainty bound being unknown of the switching function for the DSMC, an RDSMC is proposed to improve the control performance and further increase the robustness of the magnetic levitation system. In the RDSMC, an RENN estimator is used to estimate an unknown nonlinear function of lumped uncertainty online and replace the switching function in the hitting control of the DSMC directly. The adaptive learning algorithms that trained the parameters of the RENN online are derived using Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties including approximation error, optimal parameter vectors, and higher order terms in Taylor series. Finally, some experimental results of tracking the various periodic trajectories demonstrate the validity of the proposed RDSMC for practical applications.

Original languageEnglish
Pages (from-to)938-951
Number of pages14
JournalIEEE Transactions on Neural Networks
Issue number6
Publication statusPublished - 2009
Externally publishedYes


  • Dynamic sliding-mode control (DSMC)
  • Elman neural network (ENN)
  • Magnetic levitation
  • Robust control

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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