Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Automated Control Configuration Selection Considering System Uncertainties
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems. Department of Electrical Engineering, University of Kufa, Najaf, Iraq.ORCID iD: 0000-0001-7598-4815
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-9992-7791
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0002-5888-8626
Number of Authors: 3
2017 (English)In: Industrial & Engineering Chemistry Research, ISSN 0888-5885, E-ISSN 1520-5045, Vol. 56, no 12, p. 3347-3359Article in journal (Refereed) Published
Abstract [en]

This paper proposes an automated pairing approach for configuration selection of decentralized controllers which considers system uncertainties. Following the Relative Interaction Array (RIA) pairing rules, the optimal control configuration, i.e. the configuration that fits best the pairing rules, is obtained automatically by formulating the control configuration selection problem as an Assignment Problem (AP). In this AP, the associated costs related to each input-output pairing are given by the RIA coefficients. The Push-Pull algorithm is used to solve the AP for the nominal system and to obtain the set of costs for which the resulting configuration remains optimal, also called the perturbation set. The introduction of uncertainty bounds on the RIA-based costs enables the testing of the possible violation of the optimality conditions. Examples to illustrate the proposed approach for a 3×3 system and 4×4 gasifier plant are given.

Place, publisher, year, edition, pages
2017. Vol. 56, no 12, p. 3347-3359
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
URN: urn:nbn:se:ltu:diva-62421DOI: 10.1021/acs.iecr.6b04035ISI: 000398248000021Scopus ID: 2-s2.0-85019961627OAI: oai:DiVA.org:ltu-62421DiVA, id: diva2:1080714
Funder
EU, Horizon 2020, 649796EU, Horizon 2020, 636834VINNOVA
Note

Validerad; 2017; Nivå 2; 2017-03-29 (rokbeg)

Available from: 2017-03-10 Created: 2017-03-10 Last updated: 2018-05-08Bibliographically approved
In thesis
1. Selection of Decentralized Control Configuration for Uncertain Systems
Open this publication in new window or tab >>Selection of Decentralized Control Configuration for Uncertain Systems
2018 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Industrial processes nowadays involve hundreds or more of variables to be maintained within predefined ranges to achieve the production demands. However, the lack of accurate models and practical tools to design controllers for such large processes motivate the engineers/practitioners to break the processes down into smaller subsystems and applying decentralized controllers.

In contrast to the centralized controller, the decentralized controller is favourable in large-scale systems due to its robustness against loop failures and model uncertainties as well as being easier to tune and update. Yet, two steps are required prior to synthesizing these single-input single-output (SISO) controllers that comprise the decentralized controller. In the first step, a set of manipulated and the controlled variables need to be selected while the second step deals with pairing these variables to close the SISO control loops in a manner that limits the interaction between the loops. The latter step, called "input-output pairing", is usually performed by means of interaction measures (IM) tools using a nominal system model. Taking model uncertainties into consideration when deciding the pairing selection of the decentralized controller is necessary since adopting the pairing based on the nominal system model might be misleading and resulting in poor system performance or instability. It is therefore essential to have tools indicating the extent to which the pairing based on the nominal model persists against gain variations due to uncertainties.

The work in this thesis presents a methodology that determines whether the effect of gain uncertainty would invalidate the selected pairing. This has been done following the definition of the most established IM tool used in the industry, the relative gain array (RGA), and some of its variants. Further, a procedure has been developed to automatically obtain the optimal input-output pairing by formulating the pairing rules of relative interaction array (RIA) method as an \textit{assignment problem} (AP), and thus, simplifying the pairing selection for large-scale systems. Thereafter, uncertainty bounds of the RIA elements are employed to validate the pairing selection under the effect of given variations of the system gain. Moreover, following the RIA pairing rules, a method is proposed to calculate a minimum amount of uncertainty that renders a perturbed system for which the pairing, obtained from the nominal system model, becomes invalid.

In the aforementioned methodologies, a parametric system model is assumed to be known. To relax this constraint, an approach is therefore proposed and evaluated which identifies the pairing of the decentralized controller directly from the input-output data. This approach has the advantage of exempting the user from deriving a complete parametric model of the plant to decide the input-output pairing, and hence saves the efforts by finding the parameters of the most significant subsystems in a multivariable system. The frequency response of the system and its covariance, and subsequently the dynamic RGA (DRGA) and corresponding uncertainty bounds, are estimated from the input-output data by employing a nonparametric system identification approach. 

In short, the work presented in this thesis provides beneficial methodologies for researchers in academia as well as engineers in industry to predict the influence of the system gain uncertainty on the pairing selection of decentralized controllers.

Place, publisher, year, edition, pages
Luleå: Luleå University of Technology, 2018
Series
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
National Category
Engineering and Technology Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-67649 (URN)978-91-7790-053-5 (ISBN)978-91-7790-054-2 (ISBN)
Public defence
2018-04-10, A109, Luleå University of Technology, Luleå, 10:00 (English)
Opponent
Supervisors
Available from: 2018-02-14 Created: 2018-02-14 Last updated: 2018-03-23Bibliographically approved

Open Access in DiVA

fulltext(1345 kB)6 downloads
File information
File name FULLTEXT01.pdfFile size 1345 kBChecksum SHA-512
1423b99630700592b291ab07f1f40e56350bb072f7f14981c7bc126de64599e66e862d6ae9b94eaa27ae85e8f49945a32206318d58f8309c9db61714996582f6
Type fulltextMimetype application/pdf

Other links

Publisher's full textScopushttp://pubs.acs.org/doi/abs/10.1021/acs.iecr.6b04035

Search in DiVA

By author/editor
Kadhim, AliCastaño Arranz, MiguelBirk, Wolfgang
By organisation
Signals and Systems
In the same journal
Industrial & Engineering Chemistry Research
Control Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 6 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 234 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf