- -

Control predictivo de sistemas ciberfísicos

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Control predictivo de sistemas ciberfísicos

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Maestre, José María es_ES
dc.contributor.author Chanfreut, Paula es_ES
dc.contributor.author García Martín, Javier es_ES
dc.contributor.author Masero, Eva es_ES
dc.contributor.author Inoue, Masaki es_ES
dc.contributor.author Camacho, Eduardo F. es_ES
dc.date.accessioned 2021-12-21T09:06:06Z
dc.date.available 2021-12-21T09:06:06Z
dc.date.issued 2021-12-17
dc.identifier.issn 1697-7912
dc.identifier.uri http://hdl.handle.net/10251/178681
dc.description.abstract [EN] Predictive control encompasses a family of controllers that continually replan the system inputs during a certain time horizon to optimize their expected evolution according to a given criterion. This methodology has among its current challenges the adaptation to the paradigm of the so-called cyber-physical systems, which are composed of computers, sensors, actuators and physical entities of various kinds, including robots and even human beings who exchange information to control physical processes. This tutorial introduces the core concepts for the application of predictive control to cyber-physical systems by reviewing a series of examples that exploit the versatility of this design framework so as to solve the challenges presented by 21st century applications. es_ES
dc.description.abstract [ES] El control predictivo engloba a una familia de controladores que replanifican continuamente las entradas del sistema durante un cierto horizonte temporal con el fin de optimizar su evolución esperada conforme a un criterio dado. Esta metodología tiene entre sus retos actuales la adaptación al paradigma de los llamados sistemas ciberfísicos, que están compuestos por computadoras, sensores, actuadores y entidades físicas de diversa índole entre las que se incluyen robots e incluso seres humanos que intercambian información con el objetivo de controlar procesos físicos. Este tutorial presenta los conceptos centrales de la integración del control predictivo en este tipo de sistemas mediante el repaso a una serie de ejemplos que explotan la versatilidad de este marco de diseño de controladores para resolver los desafíos que presentan las aplicaciones del siglo XXI. es_ES
dc.description.sponsorship Este trabajo ha sido financiado por el European Research Council (ERC) en el marco del programa de investigación e innovación Horizonte 2020 de la Unión Europea [OCONTSOLAR, ref. 789051], por el Ministerio de Economía con el proyecto C3PO [ref. DPI2017-86918-R], por el Ministerio de Ciencia, Innovación y Universidades en el marco del programa de Formación de Profesorado Universitario (FPU) [FPU17/02653 y FPU18/04476] y por la Consejería Transformación Económica, Industria, Conocimiento y Universidades en el marco del programa de Ayudas a los agentes públicos del Sistema Andaluz del Conocimiento, para la realización de proyectos de I+D+i (PAIDI 2020) [Ampliación Aquacollect, ref. P18-HO-4713]. es_ES
dc.language Español es_ES
dc.publisher Universitat Politècnica de València es_ES
dc.relation.ispartof Revista Iberoamericana de Automática e Informática industrial es_ES
dc.rights Reconocimiento - No comercial - Compartir igual (by-nc-sa) es_ES
dc.subject Control predictivo basado en modelos es_ES
dc.subject Control de robots y sistemas multi-robot es_ES
dc.subject Sistemas ciber-físicos en control es_ES
dc.subject Interacción persona máquina en sistemas de control automático es_ES
dc.subject Control coalicional es_ES
dc.subject Model predictive control es_ES
dc.subject Robots and multi-robot systems control es_ES
dc.subject Cyber-physical systems control es_ES
dc.subject Human-machine interaction in automatic control systems es_ES
dc.subject Coalitional control es_ES
dc.title Control predictivo de sistemas ciberfísicos es_ES
dc.title.alternative Predictive Control of Cyber-Physical Systems es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/riai.2021.15771
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/789051/EU/Optimal Control of Thermal Solar Energy Systems/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-86918-R/ES/CONTROL COALICIONAL APLICADO A LA OPTIMIZACION DE SISTEMAS CIBER-FISICOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICIU//FPU17%2F02653/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MICIU//FPU18%2F04476/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Junta de Andalucía//P18-HO-4713/ es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Maestre, JM.; Chanfreut, P.; García Martín, J.; Masero, E.; Inoue, M.; Camacho, EF. (2021). Control predictivo de sistemas ciberfísicos. Revista Iberoamericana de Automática e Informática industrial. 19(1):1-12. https://doi.org/10.4995/riai.2021.15771 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/riai.2021.15771 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 12 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 19 es_ES
dc.description.issue 1 es_ES
dc.identifier.eissn 1697-7920
dc.relation.pasarela OJS\15771 es_ES
dc.contributor.funder European Commission es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder Ministerio de Ciencia, Innovación y Universidades es_ES
dc.description.references Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., Cayirci, E., 2002. Wireless Sensor Networks: A Survey. Computer Networks 38 (4), 393-422. es_ES
dc.description.references Angulo, A., Nachtmann, H., Waller, M. A., 2004. Supply Chain Information Sharing in a Vendor Managed Inventory Partnership. Journal of Business Logistics 25 (1), 101-120. es_ES
dc.description.references Badal, F. R., Das, P., Sarker, S. K., Das, S. K., 2019. A Survey on Control Issues in Renewable Energy Integration and Microgrid. Protection and Control of Modern Power Systems 4 (1), 1-27. es_ES
dc.description.references Bordons, C., García Torres, F., Valverde, L., 2015. Gestión óptima de la energía en microrredes con generación renovable. Revista Iberoamericana de Automática e Informática Industrial 12 (2), 117-132. es_ES
dc.description.references Camacho, E. F., Berenguel, M., 2012. Control of Solar Energy Systems. IFAC Proceedings Volumes 45 (15), 848-855. es_ES
dc.description.references Camacho, E. F., Berenguel, M., Rubio, F. R., 1997. Advanced Control of Solar Plants. Springer Berlin. es_ES
dc.description.references Camacho, E. F., Bordons, C., 1999. Model Predictive Control. Springer, Berlin Heidelberg. es_ES
dc.description.references Carrasco, J. M., Franquelo, L. G., Bialasiewicz, J. T., Galván, E., PortilloGuisado, R. C., Prats, M. M., Le'on, J. I., Moreno-Alfonso, N., 2006. Power-Electronic Systems for the Grid Integration of Renewable Energy Sources: A Survey. IEEE Transactions on Industrial Electronics 53 (4), 1002-1016. es_ES
dc.description.references Castilla, M., Álvarez, J. D., Berenguel, M., Pérez, M., Rodríguez, F., Guzmán, J. L., 2010. Técnicas de control del confort en edificios. Revista Iberoamericana de Automática e Informática Industrial RIAI 7 (3), 5-24. es_ES
dc.description.references Chanfreut, P., Maestre, J. M., Camacho, E. F., 2021. A Survey on Clustering Methods for Distributed and Networked Control Systems. Annual Reviews in Control. es_ES
dc.description.references DOI: https://doi.org/10.1016/j.arcontrol.2021.08.002 es_ES
dc.description.references Conde, G., Quijano, N., Ocampo-Martínez, C., 2021. Modeling and Control in Open-Channel Irrigation Systems: A Review. Annual Reviews in Control. es_ES
dc.description.references DOI: https://doi.org/10.1016/j.arcontrol.2021.01.003 es_ES
dc.description.references Dey, N., Ashour, A. S., Shi, F., Fong, S. J., Tavares, J. M., 2018. Medical Cyber-Physical Systems: A Survey. Journal of medical systems 42 (4), 1-13. es_ES
dc.description.references Fele, F., Maestre, J. M., Camacho, E. F., 2017. Coalitional Control: Cooperative Game Theory and Control. IEEE Control Systems Magazine 37 (1), 53-69. es_ES
dc.description.references Fele, F., Maestre, J. M., Hashemy, M., Mu˜noz de la Pe˜na, D., Camacho, E. F., 2014. Coalitional Model Predictive Control of an Irrigation Canal. Journal of Process Control 24 (4), 314-325. es_ES
dc.description.references DOI: https://doi.org/10.1016/j.jprocont.2014.02.005 es_ES
dc.description.references Fernández García, I., Chanfreut, P., Jurado, I., Maestre, J. M., 2021. A Data-Based Model Predictive Decision Support System for Inventory Management in Hospitals. IEEE Journal of Biomedical and Health Informatics 25 (6), 2227-2236. es_ES
dc.description.references DOI: https://doi.org/10.1109/JBHI.2020.3039692 es_ES
dc.description.references Frejo, J. R. D., Camacho, E. F., 2020. Centralized and Distributed Model Predictive Control for the Maximization of the Thermal Power of Solar Parabolic-Trough Plants. Solar Energy 204, 190-199. es_ES
dc.description.references Gil, J., Roca, L., Berenguel, M., 2020. Modelado y control automático en destilación por membranas solar: fundamentos y propuestas para su desarrollo tecnológico. Revista Iberoamericana de Automática e Informática industrial 17 (4), 329-343. es_ES
dc.description.references Guzmán, J., Acién, F., Berenguel, M., 2020. Modelado y control de la producción de microalgas en fotobiorreactores industriales. Revista Iberoamericana de Automática e Informática industrial 18 (1), 1-18. es_ES
dc.description.references Hara, K., Inoue, M., Maestre, J. M., 2020. Data-Driven Human Modeling: Quantifying Personal Tendency Toward Laziness. IEEE Control Systems Letters 5 (4), 1219-1224. es_ES
dc.description.references DOI: https://doi.org/10.1109/LCSYS.2020.3023337 es_ES
dc.description.references Hatanaka, T., Chopra, N., Fujita, M., 2015. Passivity-Based Bilateral Human-Swarm-Interactions for Cooperative Robotic Networks and Human Passivity Analysis. In: 2015 54th IEEE Conference on Decision and Control (CDC). IEEE, pp. 1033-1039. es_ES
dc.description.references DOI: https://doi.org/10.1109/CDC.2015.7402008 es_ES
dc.description.references Inoue, M., Gupta, V., 2019. "Weak" Control for Human-in-the-Loop Systems. IEEE Control Systems Letters 3 (2), 440-445. es_ES
dc.description.references Jain, A., Chakrabortty, A., Biyik, E., 2018. Distributed Wide-Area Control of Power System Oscillations under Communication and Actuation Constraints. Control Engineering Practice 74, 132-143. es_ES
dc.description.references Jianjun, S., Xu, W., Jizhen, G., Yangzhou, C., 2013. The Analysis of Traffic Control Cyber-Physical Systems. Procedia-Social and Behavioral Sciences 96, 2487-2496. es_ES
dc.description.references Jurado, I., Maestre, J. M., Velarde, P., Ocampo-Martínez, C., Fernández, I., Tejera, B. I., del Prado, J. R., 2016. Stock Management in Hospital Pharmacy Using Chance-Constrained Model Predictive Control. Computers in Biology and Medicine 72, 248-255. es_ES
dc.description.references Khamis, A., Hussein, A., Elmogy, A., 2015. Multi-robot Task Allocation: A Review of the State-of-the-art. In: Cooperative Robots and Sensor Networks. Springer, pp. 31-51. es_ES
dc.description.references Koubâa, A., Khelil, A., 2014. Cooperative Robots and Sensor Networks. Springer. es_ES
dc.description.references La Bella, A., Klaus, P., Ferrari-Trecate, G., Scattolini, R., 2021. Supervised Model Predictive Control of Large-Scale Electricity Networks via Clustering Methods. Optimal Control Applications and Methods. es_ES
dc.description.references Lee, J., Bagheri, B., Kao, H.-A., 2015. A Cyber-Physical Systems Architecture for Industry 4.0-Based Manufacturing Systems. Manufacturing letters 3, 18-23. es_ES
dc.description.references Liang, X., 2016. Emerging Power Quality Challenges due to Integration of Renewable Energy Sources. IEEE Transactions on Industry Applications 53 (2), 855-866. es_ES
dc.description.references Maestre, J. M., Fernández, M. I., Jurado, I., 2018. An application of economic model predictive control to inventory management in hospitals. Control Engineering Practice 71, 120-128. es_ES
dc.description.references Maestre, J. M., Negenborn, R. R. (Eds.), 2014. Distributed Model Predictive Control Made Easy. Vol. 69 of Intelligent Systems, Control and Automation: Science and Engineering. Springer. es_ES
dc.description.references Maestre, J. M., van Overloop, P. J., Hashemy, M., Sadowska, A., Camacho, E. F., 2014. Human in the Loop Model Predictive Control: An Irrigation Canal Case Study. In: 53rd IEEE Conference on Decision and Control. IEEE, pp. 4881-4886. es_ES
dc.description.references DOI: https://doi.org/10.1109/CDC.2014.7040151 es_ES
dc.description.references Maestre, J. M., Zafra Cabeza, A., Fernández Garcáa, M. I., Isla Tejera, B., del Prado, J. R., Camacho, E. F., 2013. Control predictivo aplicado a la gestión de stocks en farmacia hospitalaria: un enfoque orientado a la minimización del riesgo. Revista Iberoamericana de Automática e Informática industrial 10 (2), 149-158. es_ES
dc.description.references DOI: https://doi.org/10.1016/j.riai.2013.03.005 es_ES
dc.description.references Martín, J. G., Frejo, J. R. D., García, R. A., Camacho, E. F., 2021a. Multi-Robot Task Allocation Problem with Multiple Non-Linear Criteria Using Branch and Bound and Genetic Algorithms. Intelligent Service Robotics. es_ES
dc.description.references Martín, J. G., García, R. A., Camacho, E. F., 2021b. Event-MILP-Based Task Allocation for Heterogeneous Robotic Sensor Network for Thermosolar Plants. Journal of Intelligent & Robotic Systems 102 (1), 1. es_ES
dc.description.references DOI: https://doi.org/10.1007/s10846-021-01346-w es_ES
dc.description.references Martín, J. G., Maestre, J. M., Camacho, E. F., 2021c. Spatial Irradiance Estimation in a Thermosolar Power Plant by a Mobile Robot Sensor Network. Solar Energy 220, 735-744. es_ES
dc.description.references DOI: https://doi.org/10.1016/j.solener.2021.03.038 es_ES
dc.description.references Martínez, O. E. B., 2004. Evolución de una idea: de la cibernética a la cibercultura la filosofía griega y la cibernética. Cuadernos de Filosofía Latinoamericana 25 (91), 1. es_ES
dc.description.references Masero, E., Frejo, J. R. D., Maestre, J. M., Camacho, E. F., 2020. A Light Clustering Model Predictive Control Approach to Maximize Thermal Power in Solar Parabolic-Trough Plants. Solar Energy 214, 531-541. es_ES
dc.description.references Masero, E., Maestre, J. M., Camacho, E. F., 2022. Market-based clustering of model predictive controllers for maximizing collected energy by parabolictrough solar collector fields. Applied Energy 306, 117936. es_ES
dc.description.references Nagahara, M., Quevedo, D. E., Nesi'c, D., 2015. Maximum Hands-Off Control: A Paradigm of Control Effort Minimization. IEEE Transactions on Automatic Control 61 (3), 735-747. es_ES
dc.description.references DOI: https://doi.org/10.1109/TAC.2015.2452831 es_ES
dc.description.references Negenborn, R. R., Maestre, J. M., 2014. Distributed Model Predictive Control: An Overview and Roadmap of Future Research Opportunities. IEEE Control Systems Magazine 34 (4), 87-97. es_ES
dc.description.references Negenborn, R. R., van Overloop, P. J., Keviczky, T., De Schutter, B., 2009. Distributed Model Predictive Control of Irrigation Canals. Network and Heterogeneus Media 4 (2), 359-380. es_ES
dc.description.references Priess, M. C., Conway, R., Choi, J., Popovich, J. M., Radcliffe, C., 2014. Solutions to the Inverse LQR Problem with Application to Biological Systems Analysis. IEEE Transactions on Control Systems Technology 23 (2), 770-777. es_ES
dc.description.references Protte, M., Fahr, R., Quevedo, D. E., 2020. Behavioral Economics for Humanin-the-Loop Control Systems Design: Overconfidence and the Hot Hand Fallacy. IEEE Control Systems Magazine 40 (6), 57-76. es_ES
dc.description.references DOI: https://doi.org/10.1109/MCS.2020.3019723 es_ES
dc.description.references Qin, S. J., Badgwell, T. A., 2003. A Survey of Industrial Model Predictive Control Technology. Control Engineering Practice 11 (7), 733-764. es_ES
dc.description.references DOI: https://doi.org/10.1016/S0967-0661(02)00186-7 es_ES
dc.description.references Ramadan, A., Choi, J., Radcliffe, C. J., 2016. Inferring Human Subject Motor Control Intent Using Inverse MPC. In: 2016 American Control Conference (ACC). IEEE, pp. 5791-5796. es_ES
dc.description.references Ramadan, A., Choi, J., Radcliffe, C. J., Popovich, J. M., Reeves, N. P., 2018. Inferring Control Intent During Seated Balance Using Inverse Model Predictive Control. IEEE Robotics and Automation Letters 4 (2), 224-230. es_ES
dc.description.references Ramírez-Arias, A., Rodríguez, F., Guzmán, J. L., Berenguel, M., 2012. Multiobjective Hierarchical Control Architecture for Greenhouse Crop Growth. Automatica 48 (3), 490-498. es_ES
dc.description.references Sadowska, A., van Overloop, P. J., Maestre, J. M., De Schutter, B., 2015. Human-in-the-Loop Control of an Irrigation Canal Using Time Instant Optimization Model Predictive Control. In: Proceedings of the 2015 European Control Conference (ECC). IEEE, pp. 3274-3279. es_ES
dc.description.references DOI: https://doi.org/10.1109/ECC.2015.7331039 es_ES
dc.description.references Sánchez, A. J., Gallego, A. J., Escaño, J. M., Camacho, E. F., 2018. Temperature Homogenization of a Solar Trough Field for Performance Improvement. Solar Energy 165, 1-9. es_ES
dc.description.references Schmidt, M., Åhlund, C., 2018. Smart Buildings as Cyber-Physical Systems: Data-Driven Predictive Control Strategies for Energy Efficiency. Renewable and Sustainable Energy Reviews 90, 742-756. es_ES
dc.description.references Shibasaki, S., Inoue, M., Arahata, M., Gupta, V., 2020. Weak Control Approach to Consumer-Preferred Energy Management. IFAC-PapersOnLine 53 (2), 17083-17088. es_ES
dc.description.references Sun, C., Puig, V., Cembrano, G., 2020. Real-Time Control of Urban Water Cycle under Cyber-Physical Systems Framework. Water 12 (2), 406. es_ES
dc.description.references Van Overloop, P. J., Maestre, J. M., Sadowska, A. D., Camacho, E. F., De Schutter, B., 2015. Human-in-the-Loop Model Predictive Control of an Irrigation Canal [Applications of Control]. IEEE Control Systems Magazine 35 (4),19-29. es_ES
dc.description.references DOI: https://doi.org/10.1109/MCS.2015.2427040 es_ES
dc.description.references Wang, G., Gunasekaran, A., Ngai, E. W., Papadopoulos, T., 2016. Big Data Analytics in Logistics and Supply Chain Management: Certain Investigations for Research and Applications. International Journal of Production Economics 176, 98-110. es_ES
dc.description.references Wiener, N., 1948. Cybernetics or Control and Communication in the Animal and the Machine. es_ES
dc.description.references Wolf, W., 2009. Cyber-physical systems. IEEE Annals of the History of Computing 42 (03), 88-89. es_ES
dc.description.references Wu, F.-J., Kao, Y.-F., Tseng, Y.-C., 2011. From Wireless Sensor Networks Towards Cyber Physical Systems. Pervasive and Mobile computing 7 (4), 397-413. es_ES
dc.description.references Zafra-Cabeza, A., Maestre, J. M., Ridao, M. A., Camacho, E. F., Sánchez, L.,2011. A Hierarchical Distributed Model Predictive Control Approach in Irrigation Canals: A Risk Mitigation Perspective. Journal of Process Control 21 (5), 787-799. es_ES
dc.description.references DOI: https://doi.org/10.1016/j.jprocont.2010.12.012 es_ES
dc.description.references Zhong, R. Y., Newman, S. T., Huang, G. Q., Lan, S., 2016. Big Data for Supply Chain Management in the Service and Manufacturing Sectors: Challenges, Opportunities, and Future Perspectives. Computers & Industrial Engineering 101, 572-591. es_ES


Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem