Predictive control considering model uncertainty for variation reduction in multistage assembly processes

Jing Zhong, Jian Liu, Jianjun Shi

Research output: Contribution to journalArticle

26 Scopus citations

Abstract

Active control for dimensional variation reduction in multistage assembly processes (MAPs) is a challenging issue for quality assurance. It is desirable to implement a system-level control strategy to minimize the end-of-line product variance, which is propagated from upstream manufacturing stages. Research has been conducted to realize such objective, based on the variation propagation models derived from the nominal parameters of product and process design. However, due to the uncertainties induced by the significant changes of process parameters, such designated model will be different from that of the actual process, and will not precisely represent the actual physics of the process. This model discrepancy may lead to the performance deterioration of the controllers. This paper proposed a feed-forward MAP control strategy that explicitly takes into account the uncertainties of model coefficients. The case study demonstrates that, when the model uncertainties are significant, the controller derived from the proposed approach outperforms that derived without considering the model uncertainty.

Original languageEnglish (US)
Article number5382492
Pages (from-to)724-735
Number of pages12
JournalIEEE Transactions on Automation Science and Engineering
Volume7
Issue number4
DOIs
StatePublished - Oct 1 2010

Keywords

  • Model uncertainty
  • multistage assembly processes
  • predicative control
  • variation reduction

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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