Adaptive interval model control of arc welding process

John Zhang, Bruce L. Walcott

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


The welding process is typically uncertain and its dynamics may vary with welding conditions. To control this process, robust algorithms such as the interval model control proposed by Zhang and Kovacevic (1997) are needed. To improve the response speed and system performance, the authors developed an adaptive interval model control system for keyhole plasma arc welding process in this study. The developed system identifies the process parameters online, converts the identification results to the intervals in Zhang and Kovacevic's algorithm (Zhang and Kovacevic 1997), and uses a prefilter to eliminate the effect of the keyhole process' fluctuation on the control system. Experiments comparing the adaptive interval model control system with its nonadaptive counterpart have been conducted to verify the effectiveness of the former in achieving fast response speed when the manufacturing conditions or the set-point vary.

Original languageEnglish
Pages (from-to)1127-1134
Number of pages8
JournalIEEE Transactions on Control Systems Technology
Issue number6
StatePublished - Nov 2006

Bibliographical note

Funding Information:
Manuscript received January 12, 2004; revised August 9, 2005. Manuscript received in final form May 10, 2006. Recommended by Associate Editor D. A. Schoenwald. This work was supported in part by the National Science Foundation under Grant DMI-0114982 and by the University of Kentucky under the Kentucky Young Research Scholarship Program.


  • Adaptive control
  • Manufacturing
  • Robustness
  • Uncertain systems
  • Welding

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering


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