Control predictivo de sistemas ciberfísicos
DOI:
https://doi.org/10.4995/riai.2021.15771Palabras clave:
Control predictivo basado en modelo, Control de robots y sistemas multi-robot, Sistemas ciber-físicos en control, Interacción persona máquina en sistemas de control automático, Control coalicionalResumen
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.Descargas
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Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., Cayirci, E., 2002. Wireless Sensor Networks: A Survey. Computer Networks 38 (4), 393-422. https://doi.org/10.1016/S1389-1286(01)00302-4
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. https://doi.org/10.1002/j.2158-1592.2004.tb00171.x
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. https://doi.org/10.1186/s41601-019-0122-8
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. https://doi.org/10.1016/j.riai.2015.03.001
Camacho, E. F., Berenguel, M., 2012. Control of Solar Energy Systems. IFAC Proceedings Volumes 45 (15), 848-855. https://doi.org/10.3182/20120710-4-SG-2026.00181
Camacho, E. F., Berenguel, M., Rubio, F. R., 1997. Advanced Control of Solar Plants. Springer Berlin. https://doi.org/10.1007/978-1-4471-0981-5
Camacho, E. F., Bordons, C., 1999. Model Predictive Control. Springer, Berlin Heidelberg. https://doi.org/10.1007/978-1-4471-3398-8
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. https://doi.org/10.1109/TIE.2006.878356
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. https://doi.org/10.1016/S1697-7912(10)70038-8
Chanfreut, P., Maestre, J. M., Camacho, E. F., 2021. A Survey on Clustering Methods for Distributed and Networked Control Systems. Annual Reviews in Control. DOI: https://doi.org/10.1016/j.arcontrol.2021.08.002
Conde, G., Quijano, N., Ocampo-Martínez, C., 2021. Modeling and Control in Open-Channel Irrigation Systems: A Review. Annual Reviews in Control. DOI: https://doi.org/10.1016/j.arcontrol.2021.01.003
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. https://doi.org/10.1007/s10916-018-0921-x
Fele, F., Maestre, J. M., Camacho, E. F., 2017. Coalitional Control: Cooperative Game Theory and Control. IEEE Control Systems Magazine 37 (1), 53-69. https://doi.org/10.1109/MCS.2016.2621465
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. DOI: https://doi.org/10.1016/j.jprocont.2014.02.005
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. DOI: https://doi.org/10.1109/JBHI.2020.3039692
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. https://doi.org/10.1016/j.solener.2020.04.033
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. https://doi.org/10.4995/riai.2020.13122
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. https://doi.org/10.4995/riai.2020.13604
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. DOI: https://doi.org/10.1109/LCSYS.2020.3023337
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. DOI: https://doi.org/10.1109/CDC.2015.7402008
Inoue, M., Gupta, V., 2019. "Weak" Control for Human-in-the-Loop Systems. IEEE Control Systems Letters 3 (2), 440-445. https://doi.org/10.1109/LCSYS.2019.2891489
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. https://doi.org/10.1016/j.conengprac.2018.03.003
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. https://doi.org/10.1016/j.sbspro.2013.08.278
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. https://doi.org/10.1016/j.compbiomed.2015.11.011
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. https://doi.org/10.1007/978-3-319-18299-5_2
Koubâa, A., Khelil, A., 2014. Cooperative Robots and Sensor Networks. Springer. https://doi.org/10.1007/978-3-642-39301-3
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. https://doi.org/10.1002/oca.2725
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. https://doi.org/10.1016/j.mfglet.2014.12.001
Liang, X., 2016. Emerging Power Quality Challenges due to Integration of Renewable Energy Sources. IEEE Transactions on Industry Applications 53 (2), 855-866. https://doi.org/10.1109/TIA.2016.2626253
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. https://doi.org/10.1016/j.conengprac.2017.10.012
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. https://doi.org/10.1007/978-94-007-7006-5
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. DOI: https://doi.org/10.1109/CDC.2014.7040151
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. DOI: https://doi.org/10.1016/j.riai.2013.03.005
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. https://doi.org/10.1007/s11370-021-00393-4
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. DOI: https://doi.org/10.1007/s10846-021-01346-w
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. DOI: https://doi.org/10.1016/j.solener.2021.03.038
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.
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. https://doi.org/10.1016/j.solener.2020.11.056
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. https://doi.org/10.1016/j.apenergy.2021.117936
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. DOI: https://doi.org/10.1109/TAC.2015.2452831
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. https://doi.org/10.1109/MCS.2014.2320397
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. https://doi.org/10.3934/nhm.2009.4.359
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. https://doi.org/10.1109/TCST.2014.2343935
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. DOI: https://doi.org/10.1109/MCS.2020.3019723
Qin, S. J., Badgwell, T. A., 2003. A Survey of Industrial Model Predictive Control Technology. Control Engineering Practice 11 (7), 733-764. DOI: https://doi.org/10.1016/S0967-0661(02)00186-7
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. https://doi.org/10.1109/ACC.2016.7526577
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. https://doi.org/10.1109/LRA.2018.2886407
Ramírez-Arias, A., Rodríguez, F., Guzmán, J. L., Berenguel, M., 2012. Multiobjective Hierarchical Control Architecture for Greenhouse Crop Growth. Automatica 48 (3), 490-498. https://doi.org/10.1016/j.automatica.2012.01.002
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. DOI: https://doi.org/10.1109/ECC.2015.7331039
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. https://doi.org/10.1016/j.solener.2018.03.001
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. https://doi.org/10.1016/j.rser.2018.04.013
Shibasaki, S., Inoue, M., Arahata, M., Gupta, V., 2020. Weak Control Approach to Consumer-Preferred Energy Management. IFAC-PapersOnLine 53 (2), 17083-17088. https://doi.org/10.1016/j.ifacol.2020.12.1616
Sun, C., Puig, V., Cembrano, G., 2020. Real-Time Control of Urban Water Cycle under Cyber-Physical Systems Framework. Water 12 (2), 406. https://doi.org/10.3390/w12020406
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. DOI: https://doi.org/10.1109/MCS.2015.2427040
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. https://doi.org/10.1016/j.ijpe.2016.03.014
Wiener, N., 1948. Cybernetics or Control and Communication in the Animal and the Machine.
Wolf, W., 2009. Cyber-physical systems. IEEE Annals of the History of Computing 42 (03), 88-89. https://doi.org/10.1109/MC.2009.81
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. https://doi.org/10.1016/j.pmcj.2011.03.003
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. DOI: https://doi.org/10.1016/j.jprocont.2010.12.012
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. https://doi.org/10.1016/j.cie.2016.07.013
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