Robust adaptive backstepping motion control of linear ultrasonic motors using fuzzy neural network

Faa Jeng Lin, Po Huang Shieh, Po Huan Chou

Research output: Contribution to journalArticlepeer-review

66 Scopus citations

Abstract

A robust adaptive fuzzy neural network (RAFNN) backstepping control system is proposed to control the position of an X-Y-Θmotion control stage using linear ultrasonic motors (LUSMs) to track various contours in this study. First, an X-Y-Θ motion control stage is introduced. Then, the single-axis dynamics of LUSM mechanism with the introduction of a lumped uncertainty, which includes cross-coupled interference and friction force, is derived. Moreover, a conventional backstepping approach is proposed to compensate the uncertainties occurred in the motion control system. Furthermore, to improve the control performance in the tracking of the reference contours, an RAFNN backstepping control system is proposed to remove the chattering phenomena caused by the sign function in the backstepping control law. In the proposed RAFNN backstepping control system, a Sugeno-type adaptive fuzzy neural network (SAFNN) is employed to estimate the lumped uncertainty directly and a compensator is utilized to confront the reconstructed error of the SAFNN. In addition, the motions at the X axis, Y axis, and Θ axis are controlled separately. The experimental results show that the contour tracking performance is significantly improved and the robustness to parameter variations, external disturbances, cross-coupled interference, and friction force can be obtained, as well using the proposed RAFNN backstepping control system.

Original languageEnglish
Pages (from-to)676-692
Number of pages17
JournalIEEE Transactions on Fuzzy Systems
Volume16
Issue number3
DOIs
StatePublished - Jun 2008

Keywords

  • Backstepping control
  • Linear ultrasonic motors
  • Sugeno-type adaptive fuzzy neural network
  • X-Y-Θ motion contro

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