Double EWMA controller using neural network-based tuning algorithm for MIMO non-squared systems

Wei Wu, Chi Yao Maa

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

The double exponentially weighted moving average (dEWMA) control method is a popular algorithm for adjusting a process from run to run in semiconductor manufacturing. For MIMO non-squared statistic systems, the singular value decomposition (SVD) method is used for decoupling and the SVD-based dEWMA control scheme is treated as a MIMO extension of dEWMA control design. To enhance the performance and robustness of the linear system in the presence of ramp disturbances and white noises, the neural network-based adaptive algorithm is used to automatically tune the dEWMA controller parameters. Under the specified input patterns, the early stop criterion for the training-validation neural networks, and the stability constraints added in the tuning mechanism, the simulations show that the proposed control technique can effectively improve the means and standard deviations of the process outputs.

Original languageEnglish
Pages (from-to)564-572
Number of pages9
JournalJournal of Process Control
Volume21
Issue number4
DOIs
Publication statusPublished - 2011 Apr 1

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Modelling and Simulation
  • Computer Science Applications
  • Industrial and Manufacturing Engineering

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