A Design Algorithm using External Perturbation to Improve Iterative Feedback Tuning Convergence

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Abstract

Iterative Feedback Tuning constitutes an attractive control loop tuning method for processes in the absence of process insight. It is a purely data driven approach for optimization of the loop performance. The standard formulation ensures an unbiased estimate of the loop performance cost function gradient, which is used in a search algorithm for minimizing the performance cost. A slow rate of convergence of the tuning method is often experienced when tuning for disturbance rejection. This is due to a poor signal to noise ratio in the process data. A method is proposed for increasing the data information content by introducing an optimal perturbation signal in the tuning algorithm. The theoretical analysis is supported by a simulation example where the proposed method is compared to an existing method for acceleration of the convergence by use of optimal prefilters.
Original languageEnglish
JournalAutomatica
Volume47
Issue number12
Pages (from-to)2665-2670
ISSN0005-1098
DOIs
Publication statusPublished - 2011

Keywords

  • Controller tuning
  • Direct tuning
  • Iterative schemes
  • Iterative Feedback Tuning

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