Please use this identifier to cite or link to this item: https://doi.org/10.1021/ie060093e
Title: Iterative learning approach toward closed-loop automatic tuning of PID controllers
Authors: Tan, K.K. 
Zhao, S. 
Chua, K.Y.
Ho, W.K. 
Tan, W.W. 
Issue Date: 7-Jun-2006
Source: Tan, K.K.,Zhao, S.,Chua, K.Y.,Ho, W.K.,Tan, W.W. (2006-06-07). Iterative learning approach toward closed-loop automatic tuning of PID controllers. Industrial and Engineering Chemistry Research 45 (12) : 4093-4100. ScholarBank@NUS Repository. https://doi.org/10.1021/ie060093e
Abstract: This paper proposes a new method for closed-loop automatic tuning of a proportional-integral-derivative (PID) controller based on a new iterative learning control (ILC) approach. The proposed approach is applicable to process control applications where there is usually a time-delay/lag phenomenon and where nonrepetitive step changes in the reference signal are far more common than repetitive ones assumed in most literature on ILC. The method does not require the control loop to be detached for tuning, but it requires the input of a periodic reference signal which can be specified by the user or derived from a relay feedback experiment. A modified ILC scheme iteratively changes the control signal by adjusting the reference signal only. The learning gain can be selected to satisfy a necessary and sufficient condition derived in the paper, based on the information available from the oscillations induced. Once a satisfactory performance is achieved, the PID controller is then tuned by fitting the controller to yield a fitting input and output characteristic of the ILC component. Simulation and experimental results are furnished to illustrate the effectiveness of the proposed tuning method. © 2006 American Chemical Society.
Source Title: Industrial and Engineering Chemistry Research
URI: http://scholarbank.nus.edu.sg/handle/10635/56423
ISSN: 08885885
DOI: 10.1021/ie060093e
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

8
checked on Dec 13, 2017

WEB OF SCIENCETM
Citations

6
checked on Nov 1, 2017

Page view(s)

16
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.