Parallel computing for rolling mill process with a numerical treatment of the LQR problem

Computación en paralelo para el proceso de laminación con un tratamiento numérico del problema LQR

dc.creatorGómez Múnera, John Anderson
dc.creatorGiraldo Quintero, Alejandro
dc.date2023-07-21T21:03:43Z
dc.date2023-07-21T21:03:43Z
dc.date2020
dc.date.accessioned2023-10-03T20:06:05Z
dc.date.available2023-10-03T20:06:05Z
dc.identifierJ. A. Gómez & A. Giraldo-Quintero, “Parallel Computing for Rolling Mill Process with a Numerical Treatment of the LQR Problem”, J. Comput. Electron. Sci.: Theory Appl., vol. 1, no. 1, pp. 11–30, 2020. https://doi.org/10.17981/cesta.01.01.2020.02
dc.identifierhttps://hdl.handle.net/11323/10334
dc.identifier10.17981/cesta.01.01.2020.02
dc.identifier2745-0090
dc.identifierCorporación Universidad de la Costa
dc.identifierREDICUC - Repositorio CUC
dc.identifierhttps://repositorio.cuc.edu.co/
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/9174220
dc.descriptionThe considerable increase in computation of the optimal control problems has in many cases overflowed the computing capacity available to handle complex systems in real time. For this reason, alternatives such as parallel computing are studied in this article, where the problem is worked out by distributing the tasks among several processors in order to accelerate the computation and to analyze and investigate the reduction of the total time of calculation the incremental gradually the processors used in it. We explore the use of these methods with a case study represented in a rolling mill process, and in turn making use of the strategy of updating the Phase Finals values for the construction of the final penalty matrix for the solution of the differential Riccati Equation. In addition, the order of the problem studied is increasing gradually for compare the improvements achieved in the models with major dimension. Parallel computing alternatives are also studied through multiple processing elements within a single machine or in a cluster via OpenMP, which is an Application Programming Interface (API) that allows the creation of shared memory programs.
dc.descriptionEl considerable aumento en el cómputo de los problemas de control óptimo ha desbordado en muchos casos la capacidad de computación disponible para manejar sistemas complejos en tiempo real. Por esta razón, en este artículo se estudian alternativas como la computación paralela, donde el problema se resuelve distribuyendo las tareas entre varios procesadores para acelerar el cómputo y para analizar e investigar la reducción del tiempo total de cálculo incrementando gradualmente los procesadores utilizados en él. Exploramos el uso de estos métodos con un estudio de caso representado en un proceso de laminación, y a su vez haciendo uso de la estrategia de actualización de los valores de las fases finales para la construcción de la matriz de penalización final para la solución de la ecuación de Riccati diferencial. Además, el orden del problema estudiado va aumentando gradualmente para comparar las mejoras logradas en los modelos de mayor dimensión. También se estudian alternativas de computación paralela a través de múltiples elementos de procesamiento dentro de una sola máquina o en un clúster mediante OpenMP, que es una Interfaz de Programación de Aplicaciones (API) que permite la creación de programas de memoria compartida.
dc.format20 páginas
dc.formatapplication/pdf
dc.formatapplication/pdf
dc.languageeng
dc.publisherCorporación Universidad de la Costa
dc.publisherColombia
dc.relationComputer and Electronic Sciences: Theory and Applications
dc.relation[1] V. E. Sonzogni, A. M. Yommi, N. M. Nigro & M. A. Storti, “A parallel finite element program on a beowulf cluster,” Adv Eng Softw, vol. 33, no. 7, pp. 427–443, Jul. 2002. https://doi.org/10.1016/S0965-9978(02)00059-5
dc.relation[2] J. D. Hennessy & D. A. Patterson, Arquitectura de computadores: Un enfoque cuantitativo. MD, Esp.: McGraw-Hill, 1993.
dc.relation[3] M. Tokhi, M. A. Hossain & M. Shaheed, Parallel Computing for Real-Time Signal Processing and Control. London, UK: Springer, 2003.
dc.relation[4] A. S. Tanenbaum, Sistemas operativos modernos. 3rd edición. MX, D.F., MX: Pearson Educación, 2009.
dc.relation[5] P. Pardalos & R. Pytlak, Conjugate Gradient Algorithms In Nonconvex Optimization. NY, USA: Springer, 2008.
dc.relation[6] A. A. Agrachev & Y. L. Sachkov, Control Theory from the Geometric Viewpoint. Bln-HDB: Springer-Verlag, 2004.
dc.relation[7] M. Athans & P. Falb, Optimal Control: An Introduction to the Theory and Its Applications. NY, USA: Dover, 2006.
dc.relation[8] V. Costanza & P. S. Rivadeneira, Enfoque Hamiltoniano al control optimo de sistemas dinámicos. SAANZ, DE: OmniScriptum, 2014.
dc.relation[9] L. S. Pontryagin, V. G. Boltyanskii, R. V. Gamkrelidze & E. F. Mishchenko, The Mathematical Theory of Optimal Processes. NY, USA: Macmillan, 1964.
dc.relation[10] J. L. Troutman, Variational Calculus and Optimal Control. NY, USA: Springer, 1996.
dc.relation[11] V. Costanza & C. E. Neuman, “Partial differential equations for missing boundary conditions in the linear-quadratic optimal control problems,” Lat Am Appl Res, vol. 39, no. 3, pp. 207–212, Dec. 2009. Available: http://hdl.handle.net/11336/17096
dc.relation[12] E. D. Sontag, Mathematical Control Theory. NY, USA: Springer, 1998.
dc.relation[13] V. Costanza & C. E. Neuman, “Optimal control of nonlinear chemical reactors via an initial-value hamiltonian problem,” Optim Control Appl Methods, vol. 27, no. 1, pp. 41–60, Jan. 2006. http://dx.doi.org/10.1002/oca.772
dc.relation[14] A. Kojima & M. Morari, “LQ control for constrained continuous-time systems,” Automatica, vol. 40, no. 7, pp. 1143–1155, Jul. 2004. https://doi.org/10.1016/j.automatica.2004.02.007
dc.relation[15] S. J. Qin & T. A. Badgwell, “A survey of industrial model predictive control technology,” Control Eng Pract, vol. 11, no. 7, pp. 733–764, Jul. 2003. https://doi.org/10.1016/S0967-0661(02)00186-7
dc.relation[16] O. J. Rojas, G. C. Goodwin, M. M. Seron & A. Feuer, “An svd based´ strategy for receding horizon control of input constrained linear systems,” Int J Robust Nonlin, vol. 14, no. 13-14, pp. 1207–1226, May. 2004. https://doi.org/10.1002/rnc.940
dc.relation[17] J. L. Speyer & D. H. Jacobson, Primer on Optimal Control Theory. Phila, USA: SIAM Books, 2010.
dc.relation[18] V. Costanza & P. S. Rivadeneira, “Optimal satured feedback laws for LQR problems with bounded controls,” Comput Appl Math, vol. 32, no. 2, pp. 355–371, Mar. 2013. https://doi.org/10.1007/s40314-013-0025-7
dc.relation[19] V. Costanza, P. S. Rivadeneira & J. A. Gómez, “An efficient cost reduction procedure for bounded-control LQR problems,” Comput Appl Math, vol. 37, no. 2, pp. 1175–1196, Oct. 2016. https://doi.org/10.1007/s40314-016-0393-x
dc.relation[20] V. Costanza, P. S. Rivadeneira & J. A. Gómez, “Numerical treatment of the bounded-control lqr problem by updating the final phase value,” IEEE Lat Ame T, vol. 14, no. 6, pp. 2687–2692, Jun. 2016. https://doi.org/10.1109/TLA.2016.7555239
dc.relation[21] V. Costanza & P. S. Rivadeneira, “Online suboptimal control of linearized models,” Syst Sci Control Eng, vol. 2, no. 1, pp. 379–388, Dec. 2014. https://doi.org/10.1080/21642583.2014.913215
dc.relation[22] E. Bramanti, M. Bramanti, P. Stiavetti & E. Benedetti, “A frequency deconvolution procedure using a conjugate gradient minimization method with suitable constraints,” J Chemom, vol. 8, no. 6, pp. 409–421, Dec. 1994. https://doi.org/10.1002/ cem.1180080606
dc.relation[23] R. Fletcher & C. M. Reeves, “Function minimization for conjugate gradients,” Computer J, vol. 7, no. 2, pp. 149–154, Jan. 1964. https://doi.org/10.1093/comjnl/7.2.149
dc.relation[24] A. V. Rao, D. A. Benson, G. T. Huntington, C. Francolin, C. L. Darby & M. A. Patterson, “User’s manual for GPOPS: A matlab package for dynamic optimization using the gauss pseudospectral method,” UF, GVL; USA, Tech. Rep., Aug. 2008.
dc.relation[25] P. Bernhard, “Introduccion a la teoría de control Optimo,” Inst. Mat. Beppo Levi, ROS, AR, Tech. Rep., Cuaderno No. 4, 1972.
dc.relation[26] V. Costanza, P. S. Rivadeneira & R. D. Spies, “Equations for the missing boundary values in the hamiltonian formulation of optimal control problems,” J Optim Theory and Appl, vol. 149, no. 1, pp. 26–46, Jan. 2011. https://doi.org/10.1007/s10957-010- 9773-3
dc.relation[27] G. C. Goodwin, S. F. Graebe & M. E. Salgado, Control system design, vol. 240. NJ, USA: Prentice Hall, 2001.
dc.relation30
dc.relation11
dc.relation1
dc.relation1
dc.rights© The author; licensee Universidad de la Costa - CUC.
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rightshttp://purl.org/coar/access_right/c_abf2
dc.sourcehttps://revistascientificas.cuc.edu.co/CESTA/article/view/3376
dc.subjectAutomatic control
dc.subjectChemical processes
dc.subjectComputer programming
dc.subjectComputer techniques
dc.subjectMultithreading
dc.subjectParallel algorithms
dc.subjectParallel processing
dc.subjectControl automático
dc.subjectProcesos químicos
dc.subjectProgramación informática
dc.subjectTécnicas informáticas
dc.subjectMultihilo
dc.subjectAlgoritmos paralelos
dc.subjectProcesamiento paralelo
dc.titleParallel computing for rolling mill process with a numerical treatment of the LQR problem
dc.titleComputación en paralelo para el proceso de laminación con un tratamiento numérico del problema LQR
dc.typeArtículo de revista
dc.typehttp://purl.org/coar/resource_type/c_6501
dc.typeText
dc.typeinfo:eu-repo/semantics/article
dc.typehttp://purl.org/redcol/resource_type/ART
dc.typeinfo:eu-repo/semantics/publishedVersion
dc.typehttp://purl.org/coar/version/c_970fb48d4fbd8a85


Este ítem pertenece a la siguiente institución