- -

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 completo del ítem

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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/187014

Ficheros en el ítem

Metadatos del ítem

Título: Control por matriz dinámica rápido utilizando optimización en línea
Otro titulo: Fast constrained dynamic matrix control algorithm with online optimization
Autor: Peccin, Vinicius B. Lima, Daniel M. Flesch, Rodolfo C. C. Normey-Rico, Julio E.
Fecha difusión:
Resumen:
[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 ...[+]


[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 ...[+]
Palabras clave: Model Predictive Control , Optimization , Fast Processes , FPGA , Automotive systems , Control predictivo , Sistemas automotores. , Procesos rápidos , Optimización
Derechos de uso: Reconocimiento - No comercial - Compartir igual (by-nc-sa)
Fuente:
Revista Iberoamericana de Automática e Informática industrial. (issn: 1697-7912 ) (eissn: 1697-7920 )
DOI: 10.4995/riai.2022.16619
Editorial:
Universitat Politècnica de València
Versión del editor: https://doi.org/10.4995/riai.2022.16619
Código del Proyecto:
info:eu-repo/grantAgreement/CNPq//304032%2F2019-0
info:eu-repo/grantAgreement/CNPq//315546%2F2021-2
Agradecimientos:
Los autores agradecen el apoyo financiero dado por CNPq – proyectos 304032/2019-0 y 315546/2021-2.
Tipo: Artículo

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

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

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 [+]
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

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

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

Camacho, E., Bordons, C., 2004. Model Predictive Control. Advanced Text-books in Control and Signal Processing. Springer, London.

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

Cutler, C. R., Ramaker, B. L., 1980. Dynamic matrix control: A computer con-trol algorithm. In: Proc. Automatic Control Conference. Vol. 17. p. 72.

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

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.

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

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

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

Kiencke, U., Nielsen, L., 2000. Automotive Control Systems: For Engine, Dri-veline and Vehicle, 1st Edition. Springer-Verlag, Berlin, Heidelberg.

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

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

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.

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

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

Nesterov, Y., 1983. A method of solving a convex programming problem withconvergence rate o(1/k2). Soviet Mathematics Doklady 27 (2), 372-376.

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

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

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.

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

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

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

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

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

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

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

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

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

[-]

recommendations

 

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

Mostrar el registro completo del ítem