Software-based optimal PID design with robustness and noise sensitivity constraints
(2015) In Journal of Process Control 33(9). p.90-101- Abstract
- Even though PID control has been available for a long time, there are still no tuning methods including derivative action that have gained wide acceptance in industry. Also, there is still no general consensus for when one should use PID, PI or even I control on a process. The focus of this article is to present a new method for optimal PID control design that automatically picks the best controller type for the process at hand. The proposed PID design procedure uses a software-based method to find controllers with optimal or near optimal load disturbance response subject to robustness and noise sensitivity constraints. It is shown that the optimal controller type depends on maximum allowed noise sensitivity as well as process dynamics.... (More)
- Even though PID control has been available for a long time, there are still no tuning methods including derivative action that have gained wide acceptance in industry. Also, there is still no general consensus for when one should use PID, PI or even I control on a process. The focus of this article is to present a new method for optimal PID control design that automatically picks the best controller type for the process at hand. The proposed PID design procedure uses a software-based method to find controllers with optimal or near optimal load disturbance response subject to robustness and noise sensitivity constraints. It is shown that the optimal controller type depends on maximum allowed noise sensitivity as well as process dynamics. The design procedure thus results in a set of PID, PI and I controllers with different noise filters that the user can switch between to reach an acceptable control signal activity. The software is also used to compare PI and PID control performance with equivalent noise sensitivity and robustness over a large batch of processes representative for the process industry. This can be used to show how much a particular process benefits from using the derivative part. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/7651523
- author
- Garpinger, Olof LU and Hägglund, Tore LU
- organization
- publishing date
- 2015
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Software tools, PID control, Optimization, Measurement noise, Control system design
- in
- Journal of Process Control
- volume
- 33
- issue
- 9
- pages
- 90 - 101
- publisher
- Elsevier
- external identifiers
-
- wos:000361934900008
- scopus:84936876552
- ISSN
- 1873-2771
- DOI
- 10.1016/j.jprocont.2015.06.001
- language
- English
- LU publication?
- yes
- id
- c2d0b50f-d749-46c3-bbd2-d68c9f949b49 (old id 7651523)
- date added to LUP
- 2016-04-01 09:56:55
- date last changed
- 2022-04-27 17:01:08
@article{c2d0b50f-d749-46c3-bbd2-d68c9f949b49,
abstract = {{Even though PID control has been available for a long time, there are still no tuning methods including derivative action that have gained wide acceptance in industry. Also, there is still no general consensus for when one should use PID, PI or even I control on a process. The focus of this article is to present a new method for optimal PID control design that automatically picks the best controller type for the process at hand. The proposed PID design procedure uses a software-based method to find controllers with optimal or near optimal load disturbance response subject to robustness and noise sensitivity constraints. It is shown that the optimal controller type depends on maximum allowed noise sensitivity as well as process dynamics. The design procedure thus results in a set of PID, PI and I controllers with different noise filters that the user can switch between to reach an acceptable control signal activity. The software is also used to compare PI and PID control performance with equivalent noise sensitivity and robustness over a large batch of processes representative for the process industry. This can be used to show how much a particular process benefits from using the derivative part.}},
author = {{Garpinger, Olof and Hägglund, Tore}},
issn = {{1873-2771}},
keywords = {{Software tools; PID control; Optimization; Measurement noise; Control system design}},
language = {{eng}},
number = {{9}},
pages = {{90--101}},
publisher = {{Elsevier}},
series = {{Journal of Process Control}},
title = {{Software-based optimal PID design with robustness and noise sensitivity constraints}},
url = {{http://dx.doi.org/10.1016/j.jprocont.2015.06.001}},
doi = {{10.1016/j.jprocont.2015.06.001}},
volume = {{33}},
year = {{2015}},
}