Adaptive Control of Two-Axis Motion Control System Using Interval Type-2 Fuzzy Neural Network

Faa Jeng Lin, Po Huan Chou

Research output: Contribution to journalArticlepeer-review

137 Scopus citations

Abstract

An interval type-2 fuzzy neural network (IT2FNN) control system is proposed for the precision control of a two-axis motion control system in this paper. The adopted two-axis motion control system is composed of two permanent-magnet linear synchronous motors. In the proposed IT2FNN control system, an IT2FNN, which combines the merits of an interval type-2 fuzzy logic system and a neural network, is developed to approximate an unknown dynamic function. Moreover, adaptive learning algorithms that can train the parameters of the IT2FNN online are derived using the Lyapunov stability theorem. Furthermore, a robust compensator is proposed to confront the uncertainties, including a minimum reconstructed error, optimal parameter vectors, and higher order terms in Taylor series. To relax the requirement for the value of the lumped uncertainty in the robust controller, an adaptive lumped uncertainty estimation law is also investigated. Last, the proposed control algorithms are implemented in a TMS320C32 digital-signal-processor-based control computer. From the simulated and experimental results, the contour tracking performance of the two-axis motion control system is significantly improved, and the robustness can be obtained as well using the proposed IT2FNN control system.

Original languageEnglish
Pages (from-to)178-193
Number of pages16
JournalIEEE Transactions on Industrial Electronics
Volume56
Issue number1
DOIs
StatePublished - Jan 2009

Keywords

  • Lyapunov stability theorem
  • permanent-magnet linear synchronous motors (PMLSMs)
  • two-axis motion control system
  • type-2 fuzzy logic system (FLS)
  • type-2 fuzzy neural network (T2FNN)

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