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Selection of Decentralized Control Configuration for Uncertain Systems
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Signals and Systems.ORCID iD: 0000-0001-7598-4815
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: urn:nbn:se:ltu:diva-67649ISBN: 978-91-7790-053-5 (print)ISBN: 978-91-7790-054-2 (electronic)OAI: oai:DiVA.org:ltu-67649DiVA, id: diva2:1182638
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
List of papers
1. Relative Gain Array of Weakly Nonlinear Systems using a Nonparametric Identification Approach
Open this publication in new window or tab >>Relative Gain Array of Weakly Nonlinear Systems using a Nonparametric Identification Approach
2015 (English)In: 2015 IEEE International Conference on Control Applications (CCA 2015): Sydney, Australia, September 21-23, Piscataway, NJ: IEEE Communications Society, 2015, p. 1612-1617, article id 7320840Conference paper, Published paper (Refereed)
Abstract [en]

This article presents a procedure to estimate the relative gain array (RGA) matrix for weakly nonlinear systems by means of nonparametric identification of the frequency response matrix (FRM). Specifically, the best linear approximation of nonlinear systems and the covariance of the nonlinear distortions are used in the relative gain array estimation. For the estimation neither process model nor model structure need to be known which is an advantage over methods that require accurate knowledge of a parametric process model. The proposed approach is compared with the original RGA and a nonlinear RGA calculation using the well-known quadruple tank process as a case

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015
Series
I E E E International Conference on Control Applications. Proceedings, ISSN 1085-1992
National Category
Control Engineering Signal Processing
Research subject
Control Engineering; Signal Processing
Identifiers
urn:nbn:se:ltu:diva-40297 (URN)10.1109/CCA.2015.7320840 (DOI)84964355520 (Scopus ID)f5fd5d67-cccf-4910-9d34-0fbcbe476a16 (Local ID)978-1-4799-7787-1 (ISBN)f5fd5d67-cccf-4910-9d34-0fbcbe476a16 (Archive number)f5fd5d67-cccf-4910-9d34-0fbcbe476a16 (OAI)
Conference
IEEE International Conference on Control Applications : 21/09/2015 - 23/09/2015
Note
Validerad; 2016; Nivå 1; 20150717 (wolfgang)Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2018-03-19Bibliographically approved
2. Dynamic Relative Gain Array Estimation using Local Polynomial Approximation Approach
Open this publication in new window or tab >>Dynamic Relative Gain Array Estimation using Local Polynomial Approximation Approach
2016 (English)In: Modeling, Identification and Control, ISSN 0332-7353, E-ISSN 1890-1328, Vol. 37, no 4, p. 247-259Article in journal (Refereed) Published
Abstract [en]

This article presents a procedure that utilizes the local polynomial approximation approach in the estimation of the Dynamic Relative Gain Array (DRGA) matrix and its uncertainty bounds for weakly nonlinear systems. This procedure offers enhanced frequency resolution and noise reduction when random excitation is used. It also allows separation of nonlinear distortions with shorter measuring time when multisine excitation is imposed. The procedure is illustrated using the well-known quadruple tank process as a case study in simulation and in real life. Besides, a comparison with the pairing results of the static RGA, nonlinear RGA and DRGA based on linearized quadruple tank model for different simulation cases is performed.

National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-61818 (URN)10.4173/mic.2016.4.5 (DOI)000391206900005 ()2-s2.0-85013356890 (Scopus ID)
Projects
Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIREOPTi Optimisation of District Heating Cooling systems, OPTi
Funder
EU, Horizon 2020, 636834EU, Horizon 2020, 649796
Note

Validerad; 2017; Nivå 2; 2017-02-03 (andbra)

Available from: 2017-02-03 Created: 2017-02-03 Last updated: 2018-07-10Bibliographically approved
3. Relative Gain Array Variation for Norm Bounded Uncertain Systems
Open this publication in new window or tab >>Relative Gain Array Variation for Norm Bounded Uncertain Systems
2015 (English)In: IEEE 54th Annual Conference on Decision and Control (CDC): Osaka, Japan, 15-18 Dec. 2015, Piscataway, NJ: IEEE Communications Society, 2015, p. 5959-5965, article id 7403156Conference paper, Published paper (Refereed)
Abstract [en]

This article proposes computationally tractable, easyto expand and tight relative gain variation bound for uncertain systems. The proposed bound is a further development of previous work, which is summarized anddiscussed. Using several examples, the new method is compared with previous results and the advantages are highlighted. The prediction of sign changes in relative gain array elements due to uncertainties is important for pairing decisions. Based on the proposed bound,a method for the prediction of the uncertainty levels which render sign changes is suggested. The prediction method is currently limited to certain classes of systems.In this prediction method neither prior knowledge of the uncertainty nor numerous calculations are needed.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Communications Society, 2015
Series
I E E E Conference on Decision and Control. Proceedings, ISSN 0743-1546
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-27878 (URN)10.1109/CDC.2015.7403156 (DOI)000381554506025 ()84962026626 (Scopus ID)173b8281-2160-4cfe-afc7-8655ccaa121e (Local ID)978-1-4799-7884-7 (ISBN)173b8281-2160-4cfe-afc7-8655ccaa121e (Archive number)173b8281-2160-4cfe-afc7-8655ccaa121e (OAI)
Conference
IEEE Conference of Decision and Control : 16/12/2015 - 18/12/2015
Note

Godkänd; 2015; 20150726 (alikad)

Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-03-19Bibliographically approved
4. Automated Control Configuration Selection Considering System Uncertainties
Open this publication in new window or tab >>Automated Control Configuration Selection Considering System Uncertainties
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
American Chemical Society (ACS), 2017
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-62421 (URN)10.1021/acs.iecr.6b04035 (DOI)000398248000021 ()2-s2.0-85019961627 (Scopus ID)
Projects
OPTi Optimisation of District Heating Cooling systems, OPTiIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIRE
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: 2018-05-30Bibliographically approved

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