Robust PID Design by Chance-Constrained Optimization
Mercader, Pedro ; Soltesz, Kristian LU
and Baños, Alfonso
(2017)
In Journal of the Franklin Institute
354(18).
p.8217-8231
- Abstract
- A method for synthesizing proportional-integral-derivative (PID) controllers for process models with probabilistic parametric uncertainty is presented. The proposed method constitutes a stochastic extension to the well-studied maximization of integral gain optimization (MIGO) approach, i.e., maximization of integral gain under constraints on the H-infinity norm of relevant closed-loop transfer functions. The underlying chance-constrained optimization problem is solved using a gradient-based algorithm once it has been approximated by a deterministic optimization problem. The approximate solution is then probabilistically verified using randomized algorithms (RAs). The proposed method is demonstrated through several realistic synthesis... (More)
- A method for synthesizing proportional-integral-derivative (PID) controllers for process models with probabilistic parametric uncertainty is presented. The proposed method constitutes a stochastic extension to the well-studied maximization of integral gain optimization (MIGO) approach, i.e., maximization of integral gain under constraints on the H-infinity norm of relevant closed-loop transfer functions. The underlying chance-constrained optimization problem is solved using a gradient-based algorithm once it has been approximated by a deterministic optimization problem. The approximate solution is then probabilistically verified using randomized algorithms (RAs). The proposed method is demonstrated through several realistic synthesis examples. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/fc9dc96f-2a07-4a43-a06d-53994ae571c9
- author
- Mercader, Pedro
; Soltesz, Kristian
LU
and Baños, Alfonso
- organization
- publishing date
- 2017
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- PID control, Probability, Uncertainty, Sparse grid
- in
- Journal of the Franklin Institute
- volume
- 354
- issue
- 18
- pages
- 8217 - 8231
- publisher
- Elsevier
- external identifiers
-
- scopus:85033231461
- wos:000417001200012
- ISSN
- 0016-0032
- DOI
- 10.1016/j.jfranklin.2017.10.017
- project
- PID Control
- language
- English
- LU publication?
- yes
- id
- fc9dc96f-2a07-4a43-a06d-53994ae571c9
- date added to LUP
- 2017-10-05 21:00:01
- date last changed
- 2024-04-28 20:54:15
@article{fc9dc96f-2a07-4a43-a06d-53994ae571c9,
abstract = {{A method for synthesizing proportional-integral-derivative (PID) controllers for process models with probabilistic parametric uncertainty is presented. The proposed method constitutes a stochastic extension to the well-studied maximization of integral gain optimization (MIGO) approach, i.e., maximization of integral gain under constraints on the H-infinity norm of relevant closed-loop transfer functions. The underlying chance-constrained optimization problem is solved using a gradient-based algorithm once it has been approximated by a deterministic optimization problem. The approximate solution is then probabilistically verified using randomized algorithms (RAs). The proposed method is demonstrated through several realistic synthesis examples.}},
author = {{Mercader, Pedro and Soltesz, Kristian and Baños, Alfonso}},
issn = {{0016-0032}},
keywords = {{PID control; Probability; Uncertainty; Sparse grid}},
language = {{eng}},
number = {{18}},
pages = {{8217--8231}},
publisher = {{Elsevier}},
series = {{Journal of the Franklin Institute}},
title = {{Robust PID Design by Chance-Constrained Optimization}},
url = {{https://lup.lub.lu.se/search/files/32794472/soltesz17i.pdf}},
doi = {{10.1016/j.jfranklin.2017.10.017}},
volume = {{354}},
year = {{2017}},
}