Adaptive backstepping control using recurrent neural network for linear induction motor drive

Faa Jeng Lin, Rong Jong Wai, Wen Der Chou, Shu Peng Hsu

Research output: Contribution to journalArticlepeer-review

115 Scopus citations

Abstract

An adaptive backstepping control system using a recurrent neural network (RNN) is proposed to control the mover position of a linear induction motor (LIM) drive to compensate the uncertainties including the friction force in this paper. First, the dynamic model of an indirect field-oriented LIM drive is derived. Then, a backstepping approach is proposed to compensate the uncertainties including the friction force occurred in the motion control system. With the proposed backstepping control system, the mover position of the LIM drive possesses the advantages of good transient control performance and robustness to uncertainties for the tracking of periodic reference trajectories. Moreover, to further increase the robustness of the LIM drive, an RNN uncertainty observer is proposed to estimate the required lumped uncertainty in the backstepping control system. In addition, an online parameter training methodology, which is derived using the gradient-descent method, is proposed to increase the learning capability of the RNN. The effectiveness of the proposed control scheme is verified by both the simulated and experimental results.

Original languageEnglish
Pages (from-to)134-146
Number of pages13
JournalIEEE Transactions on Industrial Electronics
Volume49
Issue number1
DOIs
StatePublished - Feb 2002

Keywords

  • Adaptive backstepping control
  • Field-oriented control
  • Linear induction motor
  • Lumped uncertainty
  • Recurrent neural network

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