Neural-Fuzzy Gap Control for a Current/Voltage-Controlled 1/4-Vehicle MagLev System

This paper describes a means of obtaining gap control for magnetically levitated (Maglev) vehicles in order to better moderate such levitation as well as their propulsion dynamics. As electromagnetic levitation as is found in Maglev is highly unstable and its equilibrium region is restricted, researchers pressed a system that is modeled from two self-organizing neural-fuzzy techniques to generate both affine and linear Takagi-Sugeno (T-S) fuzzy systems. Physical systems of the Maglev such as voltage and current controllers are then coordinated through the corresponding linear-type optimized fuzzy controller. The affine-type T-S fuzzy systems were tested in simulation to determine their control properties. These systems, researchers note, provide more adjustable parameters for their neural-fuzzy learning processes and so possesses better performance in a current-controlled system like a Maglev.

  • Availability:
  • Authors:
    • Wu, Shinq-Jen
    • Wu, Cheng-Tao
    • Chang, Yen-Chen
  • Publication Date: 2008-3

Language

  • English

Media Info

Subject/Index Terms

Filing Info

  • Accession Number: 01091041
  • Record Type: Publication
  • Source Agency: UC Berkeley Transportation Library
  • Files: BTRIS, TRIS
  • Created Date: Mar 13 2008 2:10PM