A Design Algorithm using External Perturbation to Improve Iterative Feedback Tuning Convergence
Publication: Research - peer-review › Journal article – Annual report year: 2011
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 language | English |
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Journal | Automatica |
Volume | 47 |
Issue number | 12 |
Pages (from-to) | 2665-2670 |
ISSN | 0005-1098 |
DOIs | |
State | Published - 2011 |
Citations | Web of Science® Times Cited: 6 |
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- Controller tuning, Direct tuning, Iterative schemes, Iterative Feedback Tuning
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