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Arranz, Miguel CastanoORCID iD iconorcid.org/0000-0002-9992-7791
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Publications (10 of 36) 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
Castaño Arranz, M., Birk, W. & Nikolakopoulos, G. (2017). A Survey on Control Configuration Selection and New Challenges in Relation to Wireless Sensor and Actuator Networks. Paper presented at 20th IFAC World Congress, Toulouse, France, 9-14 July 2017. IFAC-PapersOnLine, 50(1), 8810-8825
Open this publication in new window or tab >>A Survey on Control Configuration Selection and New Challenges in Relation to Wireless Sensor and Actuator Networks
2017 (English)In: IFAC-PapersOnLine, ISSN 1045-0823, E-ISSN 1797-318X, Vol. 50, no 1, p. 8810-8825Article in journal (Refereed) Published
Abstract [en]

This survey on Control Configuration Selection (CCS) includes methods based on relative gains, gramian-based interaction measures, methods based on optimization schemes, plantwide control, and methods for the reconfiguration of control systems. The CCS problem is discussed, and a set of desirable properties of a CCS method are defined. Open questions and research tracks are discussed, with the focus on new challenges in relation to the emerging area of Wireless Sensors and Actuator Networks.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-66198 (URN)10.1016/j.ifacol.2017.08.1536 (DOI)000423964900456 ()
Conference
20th IFAC World Congress, Toulouse, France, 9-14 July 2017
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, 649796EU, Horizon 2020, 636834VINNOVA
Note

Konferensartikel i tidskrift

Available from: 2017-10-19 Created: 2017-10-19 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
Goldin, E., Feldman, D., Georgoulas, G., Castaño Arranz, M. & Nikolakopoulos, G. (2017). Cloud computing for big data analytics in the Process Control Industry. In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017: . Paper presented at 25th Mediterranean Conference on Control and Automation, MED 2017, University of Malta, Valletta, Malta, 3-6 July 2017 (pp. 1373-1378). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), Article ID 7984310.
Open this publication in new window or tab >>Cloud computing for big data analytics in the Process Control Industry
Show others...
2017 (English)In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1373-1378, article id 7984310Conference paper, Published paper (Refereed)
Abstract [en]

The aim of this article is to present an example of a novel cloud computing infrastructure for big data analytics in the Process Control Industry. Latest innovations in the field of Process Analyzer Techniques (PAT), big data and wireless technologies have created a new environment in which almost all stages of the industrial process can be recorded and utilized, not only for safety, but also for real time optimization. Based on analysis of historical sensor data, machine learning based optimization models can be developed and deployed in real time closed control loops. However, still the local implementation of those systems requires a huge investment in hardware and software, as a direct result of the big data nature of sensors data being recorded continuously. The current technological advancements in cloud computing for big data processing, open new opportunities for the industry, while acting as an enabler for a significant reduction in costs, making the technology available to plants of all sizes. The main contribution of this article stems from the presentation for a fist time ever of a pilot cloud based architecture for the application of a data driven modeling and optimal control configuration for the field of Process Control. As it will be presented, these developments have been carried in close relationship with the process industry and pave a way for a generalized application of the cloud based approaches, towards the future of Industry 4.0

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
Mediterranean Conference on Control and Automation, ISSN 2325-369X
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-65448 (URN)10.1109/MED.2017.7984310 (DOI)000426926300225 ()2-s2.0-85027861691 (Scopus ID)9781509045334 (ISBN)
Conference
25th Mediterranean Conference on Control and Automation, MED 2017, University of Malta, Valletta, Malta, 3-6 July 2017
Projects
Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIRE
Funder
EU, Horizon 2020, 636834
Available from: 2017-09-01 Created: 2017-09-01 Last updated: 2018-07-10Bibliographically approved
Herceg, D., Georgoulas, G., Sopasakis, P., Castaño Arranz, M., Patrinos, P. K., Bemporad, A., . . . Nikolakopoulos, G. (2017). Data-driven Modelling, Learning and Stochastic Predictive Control for the Steel Industry. In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017: . Paper presented at 25th Mediterranean Conference on Control and Automation, MED 2017, University of Malta, Valletta, Malta, 3-6 July 2017 (pp. 1361-1366). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), Article ID 7984308.
Open this publication in new window or tab >>Data-driven Modelling, Learning and Stochastic Predictive Control for the Steel Industry
Show others...
2017 (English)In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1361-1366, article id 7984308Conference paper, Published paper (Refereed)
Abstract [en]

The steel industry involves energy-intensive processessuch as combustion processes whose accurate modellingvia first principles is both challenging and unlikely to leadto accurate models let alone cast time-varying dynamics anddescribe the inevitable wear and tear. In this paper we addressthe main objective which is the reduction of energy consumptionand emissions along with the enhancement of the autonomy ofthe controlled process by online modelling and uncertaintyawarepredictive control. We propose a risk-sensitive modelselection procedure which makes use of the modern theoryof risk measures and obtain dynamical models using processdata from our experimental setting: a walking beam furnaceat Swerea MEFOS. We use a scenario-based model predictivecontroller to track given temperature references at the threeheating zones of the furnace and we train a classifier whichpredicts possible drops in the excess of Oxygen in each heatingzone below acceptable levels. This information is then used torecalibrate the controller in order to maintain a high qualityof combustion, therefore, higher thermal efficiency and loweremissions

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
Mediterranean Conference on Control and Automation, ISSN 2325-369X
Keywords
Advanced Process Control; Machine Learning; Stochastic Model Predictive Control; Risk-sensitive Model Selection; Cyber-Physical Systems
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-64960 (URN)10.1109/MED.2017.7984308 (DOI)000426926300223 ()2-s2.0-85027858483 (Scopus ID)
Conference
25th Mediterranean Conference on Control and Automation, MED 2017, University of Malta, Valletta, Malta, 3-6 July 2017
Projects
Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIRE
Funder
EU, Horizon 2020, 636834
Note

Författaruppgifterna på fulltexten/DOI är omkastade (andbra)

Available from: 2017-08-04 Created: 2017-08-04 Last updated: 2018-05-29Bibliographically approved
Jafari, H., Castaño Arranz, M., Gustafsson, T. & Nikolakopoulos, G. (2017). On Control Structure Design for a Walking Beam Furnace. In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017: . Paper presented at 25th Mediterranean Conference on Control and Automation, MED 2017, University of Malta, Valletta, Malta, 3-6 July 2017 (pp. 1355-1360). Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), Article ID 7984307.
Open this publication in new window or tab >>On Control Structure Design for a Walking Beam Furnace
2017 (English)In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1355-1360, article id 7984307Conference paper, Published paper (Refereed)
Abstract [en]

The aim of this article is to introduce a novel sparse controller design for the temperature control of an experimental walking beam furnace in steel industry. Adequate tracking of temperature references is essential for the quality of the heated slabs. However, the design of the temperature control is hindered by the multivariable (non-square) dynamic behavior of the furnace. These dynamics include significant loop interactions and time delays. Furthermore, a novel data-driven model, based on real life experimental data that relies on a subspace state representation in a closed loop approach is introduced. In the sequel, the derived model is utilized to investigate the controller's structure. By applying the relative gain array approach a decentralized feedback controller is designed. However, in spite of the optimal and sparse design of the controller, there exists interaction between loops. By analyzing the interaction between the inputs-outputs with the Σ2 Gramian-based interaction methodology, a decoupled multi-variable controller is implied. The simulation result, based on the experimental modeling of the furnace, shows that the controller can successfully decrease the interaction between the loops and track the reference temperature set-points.

Place, publisher, year, edition, pages
Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2017
Series
Mediterranean Conference on Control and Automation, ISSN 2325-369X
Keywords
Control Structure Design, Interaction Measures, Decentralized Control, Walking Beam Furnace
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-63041 (URN)10.1109/MED.2017.7984307 (DOI)000426926300222 ()2-s2.0-85027842203 (Scopus ID)978-1-5090-4533-4 (ISBN)
Conference
25th Mediterranean Conference on Control and Automation, MED 2017, University of Malta, Valletta, Malta, 3-6 July 2017
Projects
Integrated Process Control based on Distributed In-Situ Sensors into Raw Material and Energy Feedstock, DISIRE
Funder
EU, Horizon 2020, 636834
Available from: 2017-04-17 Created: 2017-04-17 Last updated: 2018-05-29Bibliographically 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
Castaño Arranz, M. & Birk, W. (2017). Online Automatic and Robust Control Configuration Selection. In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017: . Paper presented at 25th Mediterranean Conference on Control and Automation, MED 2017, University of Malta, Valletta, Malta, 3-6 July 2017 (pp. 1367-1372). Piscataway, NJ: IEEE, Article ID 7984309.
Open this publication in new window or tab >>Online Automatic and Robust Control Configuration Selection
2017 (English)In: 2017 25th Mediterranean Conference on Control and Automation, MED 2017, Piscataway, NJ: IEEE, 2017, p. 1367-1372, article id 7984309Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a complete method for automatic and robust control configuration selection for linear systems which relies upon acquired process data under gaussian noise excitation.

The selection of the configuration  is based on the estimation of the Interaction Measure named Participation Matrix. This estimation is derived with uncertainty bounds, which allows to  determine online whether the uncertainty is sufficiently low to derive a robust decision on the control configuration to be used or if the uncertainty should be reduced  by e.g. prolonging the experiment to obtain more data.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE, 2017
Series
Mediterranean Conference on Control and Automation, ISSN 2325-369X
Keywords
Interaction Measures, Control Configuration Selection, Control Structure Selection, Robust Control, Decentralized Control
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-63040 (URN)10.1109/MED.2017.7984309 (DOI)000426926300224 ()2-s2.0-85027838178 (Scopus ID)978-1-5090-4533-4 (ISBN)
Conference
25th Mediterranean Conference on Control and Automation, MED 2017, University of Malta, Valletta, Malta, 3-6 July 2017
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, 649796VINNOVA
Available from: 2017-04-17 Created: 2017-04-17 Last updated: 2018-05-29Bibliographically approved
Castaño Arranz, M. & Birk, W. (2017). Prediction Error based Interaction Measure for Control Configuration Selection in Linear and Nonlinear Systems. In: : . Paper presented at 10th IFAC International Symposium onAdvanced Control of Chemical Processes, (ADCHEM 2018), Shenyang, Liaoning, China, July 25 - 27, 2018 (pp. 446-451). Elsevier, 51
Open this publication in new window or tab >>Prediction Error based Interaction Measure for Control Configuration Selection in Linear and Nonlinear Systems
2017 (English)Conference paper, Published paper (Refereed)
Abstract [en]

This paper introduces an interaction measure, which can be applied both to linear and non-linear systems. The measure is based on the prediction error of the structurally reduced model and is denoted Prediction Error Index Array (PEIA). The linear PEIA is constructed as an extension of previous results using the $\mathcal{H}_2$-norm. The non-linear PEIA is an extension for systems represented by a model in the form of Volterra series. Additionally, the paper gives an interpretation of both linear and nonlinear PEIA as the fraction of the power of the output signal which is expressed by the reduced model resulting from  the  control configuration selection. Several examples are used to illustrate and compare the interaction measure with established methodologies, like the relative gain array, participation matrix, and Hankel Interaction Index array.

Place, publisher, year, edition, pages
Elsevier, 2017
National Category
Control Engineering
Research subject
Control Engineering
Identifiers
urn:nbn:se:ltu:diva-66326 (URN)10.1016/j.ifacol.2018.09.341 (DOI)000446604800077 ()2-s2.0-85054391486 (Scopus ID)
Conference
10th IFAC International Symposium onAdvanced Control of Chemical Processes, (ADCHEM 2018), Shenyang, Liaoning, China, July 25 - 27, 2018
Projects
OPTiWARPDISIRE
Available from: 2017-10-31 Created: 2017-10-31 Last updated: 2018-10-22Bibliographically 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: 2018-07-10Bibliographically approved
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ORCID iD: ORCID iD iconorcid.org/0000-0002-9992-7791

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