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Publications (10 of 13) Show all publications
Castaño Arranz, M., Birk, W. & Kadhim, A. (2018). On Guided and Automatic Control Configuration Selection. In: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA): . Paper presented at 22nd IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), Limassol, Cyprus, 12-15 September 2017. Piscataway, Nj: Institute of Electrical and Electronics Engineers (IEEE), F134116
Open this publication in new window or tab >>On Guided and Automatic Control Configuration Selection
2018 (English)In: IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), Piscataway, Nj: Institute of Electrical and Electronics Engineers (IEEE), 2018, Vol. F134116Conference paper, Published paper (Refereed)
Abstract [en]

This paper discusses the guided and automatic control configuration selection in large scale complex systems. Due to the trend of increasesd level of automation and connectedness which is promoted by the Industry 4.0 strategy and supported by technologies relating to cyber-physical systems and the industrial internet of things, selecting appropriate control strategies becomes increasingly important and complex. This is especially important as a control strategies will limit the achievable performance of the process system, and there are  trade-offs between complexity of the control strategies, achievable performance, vulnerability and maintainability.

The paper reviews the state of the art of methodologies that support the practitioners in taking decisions on control strategies, where two main approaches are considered, the guided one and a fully automatic one. It is shown how both approached can be conducted and examples are used to clarify the selection process.

Abstract [en]

Selecting appropriate control configurations becomes increasingly important and complex. This is due to the trend of increased level of automation and connectedness which is promoted by Industry 4.0 and supported by technologies relating to cyber-physical systems and the industrial internet of things. In this scenario, there are trade-offs between simplicity of the control configurations, achievable performance, vulnerability and maintainability. The paper reviews different state of the art tools before integrating them in guidelines and in automatic methods that support the practitioners in the design on control configurations.

Place, publisher, year, edition, pages
Piscataway, Nj: Institute of Electrical and Electronics Engineers (IEEE), 2018
Series
IEEE International Conference on Emerging Technologies and Factory Automation-ETFA, ISSN 1946-0740
Keywords
automatic control configuration selection, decentralized control, interaction measures, sparse control
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-63153 (URN)10.1109/ETFA.2017.8247700 (DOI)000427812000135 ()2-s2.0-85044482354 (Scopus ID)978-1-5090-6505-9 (ISBN)
Conference
22nd IEEE International Conference on Emerging Technologies And Factory Automation (ETFA), Limassol, Cyprus, 12-15 September 2017
Projects
OPTi Optimisation of District Heating Cooling systems, OPTiIntegrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIREWARP
Funder
VINNOVAEU, Horizon 2020, 649796EU, Horizon 2020, 636834
Available from: 2017-04-25 Created: 2017-04-25 Last updated: 2018-05-29Bibliographically approved
Kadhim, A., Castaño Arranz, M. & Birk, W. (2017). Automated Control Configuration Selection Considering System Uncertainties. Industrial & Engineering Chemistry Research, 56(12), 3347-3359
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: 2018-12-14Bibliographically approved
Castaño Arranz, M. (2017). On Guided and Automatic Control Configuration Selection: Application on a Secondary Heating System.
Open this publication in new window or tab >>On Guided and Automatic Control Configuration Selection: Application on a Secondary Heating System
2017 (English)Report (Other academic)
Abstract [en]

This technical report provides supplementary materialto the research paper entitled ”On Guided and AutomaticControl Configuration Selection”, presented at the ETFA 2017.In that paper, different Control Configuration Selection (CCS)tools are reviewed and integrated into guided and automaticCCS methodologies. The guided CCS is a heuristic step-by-stepmethodology to be applied by practitioners, while the automaticCCS methodologies target the adaptation of such heuristicsinto algorithms which can be run in a computer and assist thepractitioners in the decision making. This report summarizesthe results of applying the introduced methodologies to a reallifeprocess: the Secondary Heating System. For an introductorybackground, preliminaries, and details on the methodologies,the reader is referred to the original research paper.

Series
Technical report / Luleå University of Technology, ISSN 1402-1536
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-65819 (URN)978-91-7583-981-3 (ISBN)
Projects
WARPDISIREOPTi
Available from: 2017-09-25 Created: 2017-09-25 Last updated: 2018-05-08Bibliographically approved
Kadhim, A., Birk, W. & Castaño Arranz, M. (2016). Dynamic Relative Gain Array Estimation using Local Polynomial Approximation Approach. Modeling, Identification and Control, 37(4), 247-259
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: 2019-12-09Bibliographically approved
Kadhim, A., Castaño Arranz, M. & Birk, W. (2016). System Uncertainty Effect on Optimal Control Configuration Selection (ed.). In: (Ed.), : . Paper presented at Reglermöte 2016 : 08/06/2016 - 09/06/2016.
Open this publication in new window or tab >>System Uncertainty Effect on Optimal Control Configuration Selection
2016 (English)Conference paper, Oral presentation only (Other academic)
Abstract [en]

An approach to investigate the effect of system uncertainty on the optimal controlconfiguration selection in multivariable systems is proposed. An optimal control configuration,i.e the configuration which best agrees with input-output pairing rules according to certaininteraction measure (IM) can be obtained automatically by formulating the control configurationselection as a Transportation Problem (TP). The proposed approach then checks whetherthis optimal control configuration is valid for given system uncertainties or if a change in theconguration could be expected.

National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-35553 (URN)a20b9f2e-9788-48ea-8175-6b28687148e1 (Local ID)a20b9f2e-9788-48ea-8175-6b28687148e1 (Archive number)a20b9f2e-9788-48ea-8175-6b28687148e1 (OAI)
Conference
Reglermöte 2016 : 08/06/2016 - 09/06/2016
Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved
Kadhim, A. (2015). Estimation of the Dynamic Relative Gain Array for Control Configuration Selection (ed.). (Licentiate dissertation). Paper presented at . : Luleå tekniska universitet
Open this publication in new window or tab >>Estimation of the Dynamic Relative Gain Array for Control Configuration Selection
2015 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The control of multi-input multi-output systems (MIMO) is more difficult than for single-input single-output systems (SISO) due to the multitude of input-output couplings. Coupling, simply means that a change in any input leads to changes in many outputs. Nevertheless, in many cases, a simple decentralised controller is usually sufficient to achieve desired performance goals. However, there is a need for systematic techniques that can suggest the most promising configurations or pairings for the decentralized controller.The relative gain array (RGA) has proven itself to be an efficient tool to solve the pairing problem. It is easily calculated and does not depend on input-output scaling. However, it gives misleading results in some cases where system dynamics are involved and hence Dynamic Relative Gain Array (DRGA) used instead. The commonplace procedure to estimate DRGA values from the input-output data is to identify a parametric system model. Thus, the user needs to decide a model structure and a model order to calculate the system frequency response. Eventually, DRGA values are obtained based on that system frequency response over the frequency range of interest. In this work, a method which requires less user interaction is proposed. The system frequency response, and subsequently the DRGA, is directly estimated from the input-output data by employing a non-parametric identification approach. Such an approach reduces the uncertainties arising from incorrect user decisions by avoiding the parametric model identification. However, DRGA values obtained by the nonparametric identification are subject to different uncertainty sources such as system nonlinearity and noise. In this thesis various strategies are presented to reduce the effect of these uncertainties. In that direction, RGA (DRGA) of linear systems is first analysed using a random excitation signal. Due to the nonperiodic nature of the random signal, the frequency response is susceptible to leakage. To reduce the leakage effect, data is divided into sub-records and the frequency response was averaged over these sub-records. Although the data division proved to be efficient in limiting the leakage effect it has a drawback of reducing the frequency resolution. Moreover, RGA (DRGA) of weakly nonlinear systems is analysed using a multisine excitation signal. The multisine excitation is used to distinguish between the nonlinear distortion and the output noise. It is very difficult to make such distinction using the random excitation. However, long experimental time is needed in returns. To overcome the shortcomings represented by low frequency resolution and the experiment running time, local polynomial approximation approach (LPA) is investigated using both random and multisine excitation.In that direction, RGA (DRGA) of linear systems is first analysed using a random excitation signal. Due to the nonperiodic nature of the random signal, the frequency response is susceptible to leakage. To reduce the leakage effect, data is divided into sub-records and the frequency response was averaged over these sub-records. Although the data division proved to be efficient in limiting the leakage effect it has a drawback of reducing the frequency resolution. Moreover, RGA (DRGA) of weakly nonlinear systems is analysed using a multisine excitation signal. The multisine excitation is used to distinguish between the nonlinear distortion and the output noise. It is very difficult to make such distinction using the random excitation. However, long experimental time is needed in returns. To overcome the shortcomings represented by low frequency resolution and the experiment running time, local polynomial approximation approach (LPA) is investigated using both random and multisine excitation.It can be concluded that the proposed approach achieves quite accurate RGA values with the advantage of exempting the user from deriving a complete parametric model of the plant. Hence, efforts of identifying the parameters of all MIMO subsystems can be saved by finding the parameters of the most significant subsystems of a multivariable system.

Place, publisher, year, edition, pages
Luleå tekniska universitet, 2015. p. 90
Series
Licentiate thesis / Luleå University of Technology, ISSN 1402-1757
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-26177 (URN)d06e8e1d-e35d-4908-8121-4ffe68cd8b0e (Local ID)978-91-7583-455-9 (ISBN)978-91-7583-456-6 (ISBN)d06e8e1d-e35d-4908-8121-4ffe68cd8b0e (Archive number)d06e8e1d-e35d-4908-8121-4ffe68cd8b0e (OAI)
Note
Godkänd; 2015; 20151027 (alikad); Nedanstående person kommer att hålla licentiatseminarium för avläggande av teknologie licentiatexamen. Namn: Ali Mohammed Hussein Kadhim Ämne: Reglerteknik/Control Engineering Uppsats: Estimation of the Dynamic Relative Gain Array for Control Configuration Selection Examinator: Professor Wofgang Birk, Institutionen för system- och rymdteknik, Avdelning: Signaler och system, Luleå tekniska universitet Diskutant: Associate Professor Hamid Reza Shaker, The Maersk Mc-Kinney Moller Institute, Denmark Tid: Onsdag 4 december 2015 kl 09.30 Plats: F531, Luleå tekniska universitetAvailable from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-24Bibliographically approved
Kadhim, A., Birk, W. & Gustafsson, T. (2015). Relative Gain Array Estimation Based on Non-parametric Process Identification for Uncertain Systems (ed.). Paper presented at Nordic Process Control Workshop : 16/01/2015 - 17/01/2015. Paper presented at Nordic Process Control Workshop : 16/01/2015 - 17/01/2015.
Open this publication in new window or tab >>Relative Gain Array Estimation Based on Non-parametric Process Identification for Uncertain Systems
2015 (English)Conference paper, Poster (with or without abstract) (Other academic)
Abstract [en]

Since the introduction of the Relative Gain Array (RGA) by Bristol in 1966, it has become awidely used practical tool for solving the input-output pairing problems in decentralized control. In order to remove the dependency of this tool on a parametric description and accurate knowledge of a nominal model, this work proposes a method to estimate the RGA directly from a non-parametric frequency response matrix (FRM) derived from a frequency domain system identification approach. The proposed method reduces the influence of model uncertainties on the calculation of the RGA and derives the RGA at frequencies of interest. The results are exemplified using a 2x3 LTI systems and a 2x2 uncertain system.

National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-40606 (URN)fcb517f0-f745-4ec9-818a-51dc6c824646 (Local ID)fcb517f0-f745-4ec9-818a-51dc6c824646 (Archive number)fcb517f0-f745-4ec9-818a-51dc6c824646 (OAI)
Conference
Nordic Process Control Workshop : 16/01/2015 - 17/01/2015
Note
Godkänd; 2015; 20150726 (alikad)Available from: 2016-10-03 Created: 2016-10-03 Last updated: 2017-11-25Bibliographically approved
Kadhim, A., Castano, M., Birk, W. & Gustafsson, T. (2015). Relative Gain Array of Weakly Nonlinear Systems using a Nonparametric Identification Approach (ed.). In: (Ed.), (Ed.), 2015 IEEE International Conference on Control Applications (CCA 2015): Sydney, Australia, September 21-23. Paper presented at IEEE International Conference on Control Applications : 21/09/2015 - 23/09/2015 (pp. 1612-1617). Piscataway, NJ: IEEE Communications Society, Article ID 7320840.
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
Kadhim, A., Birk, W. & Gustafsson, T. (2015). Relative Gain Array Variation for Norm Bounded Uncertain Systems (ed.). In: (Ed.), IEEE 54th Annual Conference on Decision and Control (CDC): Osaka, Japan, 15-18 Dec. 2015. Paper presented at IEEE Conference of Decision and Control : 16/12/2015 - 18/12/2015 (pp. 5959-5965). Piscataway, NJ: IEEE Communications Society, Article ID 7403156.
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
Kadhim, A., Birk, W. & Gustafsson, T. (2014). Calculation of Relative Gain Array Based on Nonparametric Process Identification: A Frequency Domain Approach (ed.). Paper presented at Reglermöte 2014 : 03/06/2014 - 04/06/2014. Paper presented at Reglermöte 2014 : 03/06/2014 - 04/06/2014.
Open this publication in new window or tab >>Calculation of Relative Gain Array Based on Nonparametric Process Identification: A Frequency Domain Approach
2014 (English)Conference paper, Oral presentation only (Other academic)
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-34208 (URN)857a27d3-4764-4215-8c31-cdfc5f68aa17 (Local ID)857a27d3-4764-4215-8c31-cdfc5f68aa17 (Archive number)857a27d3-4764-4215-8c31-cdfc5f68aa17 (OAI)
Conference
Reglermöte 2014 : 03/06/2014 - 04/06/2014
Note
Godkänd; 2014; 20141121 (andbra)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2017-11-25Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0001-7598-4815

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