- -

Control por matriz dinámica rápido utilizando optimización en línea

RiuNet: Repositorio Institucional de la Universidad Politécnica de Valencia

Compartir/Enviar a

Citas

Estadísticas

  • Estadisticas de Uso

Control por matriz dinámica rápido utilizando optimización en línea

Mostrar el registro sencillo del ítem

Ficheros en el ítem

dc.contributor.author Peccin, Vinicius B. es_ES
dc.contributor.author Lima, Daniel M. es_ES
dc.contributor.author Flesch, Rodolfo C. C. es_ES
dc.contributor.author Normey-Rico, Julio E. es_ES
dc.date.accessioned 2022-10-05T06:13:47Z
dc.date.available 2022-10-05T06:13:47Z
dc.date.issued 2022-06-29
dc.identifier.issn 1697-7912
dc.identifier.uri http://hdl.handle.net/10251/187014
dc.description.abstract [EN] This work proposes a predictive control technique to be applied in fast processes using online optimization. Currently, advanced controllers are increasingly needed in industry at low levels of automation, which are associated with sampling times on the order of milliseconds or microseconds. The quadratic programming resulting from the control problem of a predictive control algorithm with constraints, such as Dynamic Matrix Control (DMC), which is one of the most used alternatives in industry, can be considered computationally expensive and becomes a limitation to embed and use the DMC in plants with fast sample rates. This paper proposes a solution to this problem based on the dual accelerated gradient projection method, which has shorter convergence times than other solutions in the literature based on predictive control strategies with online optimization. The proposed approach was tested in simulation to control a semiactive automotive suspension system, which has fast dynamics, showing that satisfactory results can be obtained with a sampling time of 5 ms. Moreover, the proposed control was implemented in a field-programmable gate array (FPGA) and the resulting quadratic programming problem was calculated in microseconds, which allows the use of the DMC to control very fast processes. es_ES
dc.description.abstract [ES] Este trabajo propone una técnica de control predictivo para ser aplicada en procesos rápidos utilizando optimización en línea. Actualmente, en el sector industrial, los controladores avanzados son cada vez más necesarios en los bajos niveles de automatización, que están asociados con tiempos de muestreo del orden de milisegundos o microsegundos. La programación cuadrática resultante del problema de control de un algoritmo de control predictivo con restricciones, como por ejemplo el control por matriz dinámica (en inglés Dynamic Matrix Control - DMC), que es uno de los más usados en la industria, se puede considerar computacionalmente costosa y se convierte en una limitación para empotrar y usar el DMC en plantas con tasas de muestreo rápidas. Este artículo propone una solución a este problema basada en el método de proyección de gradiente acelerada dual, que tiene tiempos de convergencia menores que otras soluciones de la literatura basadas en estrategias de control predictivo con optimización en línea. Además, el control propuesto se implementó en una matriz de puertos programables (en inglés field-programmable gate array FPGA) y el problema de programación cuadrática resultante se calculó en microsegundos, lo que permite el uso del DMC en procesos muy rápidos. es_ES
dc.description.sponsorship Los autores agradecen el apoyo financiero dado por CNPq – proyectos 304032/2019-0 y 315546/2021-2. 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 Model Predictive Control es_ES
dc.subject Optimization es_ES
dc.subject Fast Processes es_ES
dc.subject FPGA es_ES
dc.subject Automotive systems es_ES
dc.subject Control predictivo es_ES
dc.subject Sistemas automotores. es_ES
dc.subject Procesos rápidos es_ES
dc.subject Optimización es_ES
dc.title Control por matriz dinámica rápido utilizando optimización en línea es_ES
dc.title.alternative Fast constrained dynamic matrix control algorithm with online optimization es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.4995/riai.2022.16619
dc.relation.projectID info:eu-repo/grantAgreement/CNPq//304032%2F2019-0 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CNPq//315546%2F2021-2 es_ES
dc.rights.accessRights Abierto es_ES
dc.description.bibliographicCitation Peccin, VB.; Lima, DM.; Flesch, RCC.; Normey-Rico, JE. (2022). Control por matriz dinámica rápido utilizando optimización en línea. Revista Iberoamericana de Automática e Informática industrial. 19(3):330-342. https://doi.org/10.4995/riai.2022.16619 es_ES
dc.description.accrualMethod OJS es_ES
dc.relation.publisherversion https://doi.org/10.4995/riai.2022.16619 es_ES
dc.description.upvformatpinicio 330 es_ES
dc.description.upvformatpfin 342 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 19 es_ES
dc.description.issue 3 es_ES
dc.identifier.eissn 1697-7920
dc.relation.pasarela OJS\16619 es_ES
dc.contributor.funder Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil es_ES
dc.description.references Ahmadi-Moshkenani, P., Johansen, T. A., Olaru, S., 2018. Combinatorial ap-proach toward multiparametric quadratic programming based on characteri-zing adjacent critical regions 63 (10), 3221-3231. https://doi.org/10.1109/TAC.2018.2791479 es_ES
dc.description.references Borrelli, F., Baoti ́c, M., Pekar, J., Stewart, G., 2010. On the computation oflinear model predictive control laws. Automatica 46 (6), 1035 - 1041. https://doi.org/10.1016/j.automatica.2010.02.031 es_ES
dc.description.references Cairano, S. D., Brand, M., Bortoff, S. A., 2013. Projection-free parallel quadra-tic programming for linear model predictive control. Int. J. Control 86 (8),1367-1385. https://doi.org/10.1080/00207179.2013.801080 es_ES
dc.description.references Camacho, E., Bordons, C., 2004. Model Predictive Control. Advanced Text-books in Control and Signal Processing. Springer, London. es_ES
dc.description.references Cimini, G., Bemporad, A., Dec 2017. Exact complexity certification of active-set methods for quadratic programming. IEEE Trans. Automat. Contr.62 (12), 6094-6109. https://doi.org/10.1109/TAC.2017.2696742 es_ES
dc.description.references Cutler, C. R., Ramaker, B. L., 1980. Dynamic matrix control: A computer con-trol algorithm. In: Proc. Automatic Control Conference. Vol. 17. p. 72. es_ES
dc.description.references Fernandes, D., Haque, M. E., Palanki, S., Rios, S. G., Chen, D., 2020. DMCcontroller design for an integrated allam cycle and air separation plant. Com-put. Chem. Eng. 141, 107019. https://doi.org/10.1016/j.compchemeng.2020.107019 es_ES
dc.description.references Ferreau, H., Almér, S., Verschueren, R., Diehl, M., Frick, D., Domahidi, A.,Jerez, J., Stathopoulos, G., Jones, C., Dec 2017. Embedded optimizationmethods for industrial automatic control. In: Proc. 20th IFAC World Congr.Toulouse, France. es_ES
dc.description.references Goldstein, T., O'Donoghue, B., Setzer, S., Baraniuk, R., 2014. Fast alternatingdirection optimization methods. SIAM J. on Imaging Sciences 7 (3), 1588-1623. https://doi.org/10.1137/120896219 es_ES
dc.description.references He, X., Lima, F. V., 2019. Development and implementation of advanced con-trol strategies for power plant cycling with carbon capture. Comput. Chem.Eng. 121, 497 - 509. https://doi.org/10.1016/j.compchemeng.2018.11.004 es_ES
dc.description.references Herceg, M., Jones, C. N., Morari, M., 2015. Dominant speed factors of activeset methods for fast MPC. Optim. Contr. Appl. Met. 36 (5), 608-627. https://doi.org/10.1002/oca.2140 es_ES
dc.description.references Kiencke, U., Nielsen, L., 2000. Automotive Control Systems: For Engine, Dri-veline and Vehicle, 1st Edition. Springer-Verlag, Berlin, Heidelberg. es_ES
dc.description.references Kvasnica, M., Tak ́acs, B., Holaza, J., Di Cairano, S., 2015. On region-free ex-plicit model predictive control. In: Proc. 54th IEEE Conf. on Decision andControl (CDC). pp. 3669-3674. https://doi.org/10.1109/CDC.2015.7402788 es_ES
dc.description.references Lee, J. H., Morari, M., Garcia, C. E., 1994. State-space interpretation of modelpredictive control. Automatica 30 (4), 707 - 717. https://doi.org/10.1016/0005-1098(94)90159-7 es_ES
dc.description.references Lima, D. M., Normey-Rico, J. E., Plucenio, A., Santos, T. L. M., Gomes, M. V.,2014. Improving robustness and disturbance rejection performance with in-dustrial MPC. In: Proc. 20th Brazilian Conference on Automation (CBA).pp. 3229-3236. es_ES
dc.description.references Morato, M. M., Normey-Rico, J. E., Sename, O., 2021. An input-to-state stablemodel predictive control framework for Lipschitz nonlinear parameter var-ying systems. International Journal of Robust and Nonlinear Control 31 (17),8239-8272. https://doi.org/10.1002/rnc.5243 es_ES
dc.description.references Morato, M. M., Q., N. M., Sename, O., Dugard, L., 2019. Design of a fastreal-time LPV model predictive control system for semi-active suspensioncontrol of a full vehicle. Journal of the Franklin Institute 356 (3), 1196-1224. https://doi.org/10.1016/j.jfranklin.2018.11.016 es_ES
dc.description.references Nesterov, Y., 1983. A method of solving a convex programming problem withconvergence rate o(1/k2). Soviet Mathematics Doklady 27 (2), 372-376. es_ES
dc.description.references O'Donoghue, B., Stathopoulos, G., Boyd, S., Nov 2013. A splitting method foroptimal control 21 (6), 2432-2442.Patrinos, P., Bemporad, A., Jan 2014. An accelerated dual gradient-projectionalgorithm for embedded linear model predictive control 59 (1), 18-33. https://doi.org/10.1109/TAC.2013.2275667 es_ES
dc.description.references Patrinos, P., Bemporad, A., Jan 2014. An accelerated dual gradient-projectionalgorithm for embedded linear model predictive control 59 (1), 18-33. https://doi.org/10.1109/TAC.2013.2275667 es_ES
dc.description.references Peccin, V. B., Lima, D. M., Flesch, R. C. C., Normey-Rico, J. E., 2019. Fastgeneralized predictive control based on accelerated dual gradient projectionmethod. In: Proc. 12th IFAC Symposium on Dynamics and Control of Pro-cess Systems, including Biosystems (DYCOPS). pp. 474-479. es_ES
dc.description.references Peccin, V. B., Lima, D. M., Flesch, R. C. C., Normey-Rico, J. E., 2020. Fast constrained generalized predictive control with ADMM embedded in an FP-GA. IEEE Latin America Trans. 18 (2), 422-429. https://doi.org/10.1109/TLA.2020.9085299 es_ES
dc.description.references Peccin, V. B., Lima, D. M., Flesch, R. C. C., Normey-Rico, J. E., 2021. Fastalgorithms for constrained generalised predictive control with on-line opti-misation. IET Control Theory & Applications 15 (4), 545-558. https://doi.org/10.1049/cth2.12060 es_ES
dc.description.references Pistikopoulos, E. N., Diangelakis, N. A., Oberdieck, R., Papathanasiou, M. M.,Nascu, I., Sun, M., 2015. PAROC""An integrated framework and softwareplatform for the optimisation and advanced model-based control of processsystems. Chem. Eng. Science 136, 115-138. https://doi.org/10.1016/j.ces.2015.02.030 es_ES
dc.description.references Pu, Y., Zeilinger, M. N., Jones, C. N., Feb 2017. Complexity certification of thefast alternating minimization algorithm for linear MPC 62 (2), 888-893. https://doi.org/10.1109/TAC.2016.2561407 es_ES
dc.description.references Roldao-Lopes, A., Shahzad, A., Constantinides, G. A., Kerrigan, E. C., April2009. More flops or more precision? Accuracy parameterizable linear equa-tion solvers for model predictive control. In: Proc. 17th IEEE Symposiumon Field-Programmable Custom Computing Machines. pp. 209-216. https://doi.org/10.1109/FCCM.2009.19 es_ES
dc.description.references Wang, J., Xu, Z., Song, C., Yao, Y., Zhao, J., 2020. A distributed model pre-dictive control algorithm with the gap metric output feedback decoupling.Comput. Chem. Eng., 107167. https://doi.org/10.1016/j.compchemeng.2020.107167 es_ES
dc.description.references Wang, Y., Boyd, S., March 2010. Fast model predictive control using onlineoptimization. IEEE Transactions on Control Systems Technology 18 (2),267-278. https://doi.org/10.1109/TCST.2009.2017934 es_ES
dc.description.references Wills, A., Mills, A., Ninness, B., 2011. FPGA implementation of an interior-point solution for linear model predictive control. In: Proc. 18th IFAC World Congress. https://doi.org/10.3182/20110828-6-IT-1002.02857 es_ES
dc.description.references Wojtulewicz, A., Ławry ́nczuk, M., 2018. Implementation of multiple-input multiple-output dynamic matrix control algorithm for fast processes usingfield programmable gate array. In: Proc. 15th IFAC Conference on Program-mable Devices and Embedded Systems (PDeS). pp. 324 - 329. https://doi.org/10.1016/j.ifacol.2018.07.174 es_ES


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

Mostrar el registro sencillo del ítem