An automatic tuner with short experiment and probabilistic plant parameterization
Soltesz, Kristian LU
; Mercader, Pedro
and Baños, Alfonso
(2017)
In International Journal of Robust and Nonlinear Control
27(11).
p.1857-1873
- Abstract
- A novel automatic tuning strategy is proposed. It is based on an experiment of very short duration, followed by simultaneous identification of LTI model parameters and an estimate of their error covariance. The parametric uncertainty model is subsequently exploited to design linear controllers with magnitude bounds on some closed-loop transfer function of interest, such as the sensitivity function. The method is demonstrated through industrially relevant examples. Robustness is enforced through probabilistic constraints on the H∞ norms of the sensitivity function, while minimizing load disturbance integral error (IE) to ensure performance. To demonstrate the strength of the proposed method, identification for the mentioned examples is... (More)
- A novel automatic tuning strategy is proposed. It is based on an experiment of very short duration, followed by simultaneous identification of LTI model parameters and an estimate of their error covariance. The parametric uncertainty model is subsequently exploited to design linear controllers with magnitude bounds on some closed-loop transfer function of interest, such as the sensitivity function. The method is demonstrated through industrially relevant examples. Robustness is enforced through probabilistic constraints on the H∞ norms of the sensitivity function, while minimizing load disturbance integral error (IE) to ensure performance. To demonstrate the strength of the proposed method, identification for the mentioned examples is carried out under a high level of measurement noise. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/e38e43cb-7c93-4574-b49a-ce1118446137
- author
- Soltesz, Kristian
LU
; Mercader, Pedro
and Baños, Alfonso
- organization
- publishing date
- 2017
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Automatic tuning, robust identification, parametric uncertainty, uncertainty propagation
- in
- International Journal of Robust and Nonlinear Control
- volume
- 27
- issue
- 11
- pages
- 1857 - 1873
- publisher
- John Wiley & Sons Inc.
- external identifiers
-
- scopus:84992189565
- wos:000404857700001
- ISSN
- 1099-1239
- DOI
- 10.1002/rnc.3640
- project
- PID Control
- Automatic Tuning
- language
- English
- LU publication?
- yes
- id
- e38e43cb-7c93-4574-b49a-ce1118446137
- date added to LUP
- 2016-08-18 17:39:53
- date last changed
- 2024-04-19 07:13:49
@article{e38e43cb-7c93-4574-b49a-ce1118446137,
abstract = {{A novel automatic tuning strategy is proposed. It is based on an experiment of very short duration, followed by simultaneous identification of LTI model parameters and an estimate of their error covariance. The parametric uncertainty model is subsequently exploited to design linear controllers with magnitude bounds on some closed-loop transfer function of interest, such as the sensitivity function. The method is demonstrated through industrially relevant examples. Robustness is enforced through probabilistic constraints on the H∞ norms of the sensitivity function, while minimizing load disturbance integral error (IE) to ensure performance. To demonstrate the strength of the proposed method, identification for the mentioned examples is carried out under a high level of measurement noise.}},
author = {{Soltesz, Kristian and Mercader, Pedro and Baños, Alfonso}},
issn = {{1099-1239}},
keywords = {{Automatic tuning; robust identification; parametric uncertainty; uncertainty propagation}},
language = {{eng}},
number = {{11}},
pages = {{1857--1873}},
publisher = {{John Wiley & Sons Inc.}},
series = {{International Journal of Robust and Nonlinear Control}},
title = {{An automatic tuner with short experiment and probabilistic plant parameterization}},
url = {{https://lup.lub.lu.se/search/files/16435277/soltesz16.pdf}},
doi = {{10.1002/rnc.3640}},
volume = {{27}},
year = {{2017}},
}