Neurofuzzy model-based predictive control of weld fusion zone geometry

Yu M. Zhang, Radovan Kovacevic

Research output: Contribution to journalArticlepeer-review

75 Scopus citations

Abstract

A closed-loop system is developed to control the weld fusion, which is specified by the top-side and back-side bead widths of the weld pool. Because in many applications only a top-side sensor is allowed, which is attached to and moves with the welding torch, an image processing algorithm and neurofuzzy model have been incorporated to measure and estimate the top-side and back-side bead widths based on an advanced top-side vision sensor. The welding current and speed are selected as the control variables. It is found that the correlation between any output and input depends on the value of another input. This cross coupling implies that a nonlinearity exists in the process being controlled. A neurofuzzy model is used to model this nonlinear dynamic process. Based on the dynamic fuzzy model, a predictive control system has been developed to control the welding process. Experiments confirmed that the developed control system is effective in achieving the desired fusion state despite the different disturbances.

Original languageEnglish
Pages (from-to)389-401
Number of pages13
JournalIEEE Transactions on Fuzzy Systems
Volume6
Issue number3
DOIs
StatePublished - 1998

Bibliographical note

Funding Information:
Manuscript received July 26, 1996; revised October 28, 1997. This work was supported by the National Science Foundation under Contract DMI-9634735, the Allison Engine Company, Indianapolis, IN, and the Center for Robotics and Manufacturing Systems, University of Kentucky, Lexington, KY.

Keywords

  • Fuzzy control
  • Modeling
  • Predictive control
  • Welding

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computational Theory and Mathematics
  • Artificial Intelligence
  • Applied Mathematics

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