Metodología de Ajuste de un NMPC con Sistema de Inferencia Borrosa Takagi Sugeno y Conjuntos Borrosos Multidimensionales para Aplicaciones en Procesos Químicos no Lineales
DOI:
https://doi.org/10.4995/riai.2018.9773Palabras clave:
Control borroso y sistemas borrosos, control de procesos industriales, Identificación de sistemas y estimación de parámetros.Resumen
En este artículo se presenta una metodología para la sintonización de los parámetros de un controlador predictivo no lineal basado en modelo (NMPC) basado en una técnica de optimización, aplicado en el control de procesos químicos no lineales, con el propósito de facilitar la etapa de determinación de los valores de ajuste de este tipo de estrategias de control. Los resultados permiten determinar el desempeño de los NMPC sintonizados con la propuesta metodológica planteada y se validan los resultados con sintonizaciones desarrolladas mediante otras estrategias. La metodología propuesta se aplica al control de la concentración de un reactivo en un tanque reactor continuamente agitado (CSTR).
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Adeli, H., Cheng, N.-T., 1993. Integrated Genetic Algorithm for Optimization of Space Structures. Journal of Aerospace Engineering 6 (4), 315–328. https://doi.org/10.1061/(ASCE)0893-1321(1993)6:4(315)
Ali, E., Al-Ghazzawi, A., 2003. On-line tuning of model predictive controllers using fuzzy logic. Canadian Journal of Chemical Engineering 81 (5), 1041– 1051. https://doi.org/10.1002/cjce.5450810515
Alvarez, 2000. Control predictivo basado en modelo borroso para el control de pH. Serie Temas de Automática. 10.
Alvarez, H., Lamanna, R., Vega, P., Revollar, S., jul 2009. Metodología para la Obtención de Modelos Semifísicos de Base Fenomenológica Aplicada a una Sulfitadora de Jugo de Ca-a de Azúcar. Revista Iberoamericana de Automática e Informática Industrial RIAI 6 (3), 10–20. https://doi.org/10.1016/S1697-7912(09)70260-2
Alvarez, H., Pe-a, M., 2004. Modelamiento de Sistemas de Inferencia Borrosa Tipo Takagi Sugeno. Avances en Sistemas e Informatica 1, 1–11.
Ammar, M. E., apr 2016. Tuning model predictive controllers for crossdirection processes. In: 2016 International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, pp. 746–751. https://doi.org/10.1109/CoDIT.2016.7593656
Banerjee, P., Shah, S. L., 1992. Tuning Guidelines for Robust Generalized Predictive Control. Proceedings of the 31st IEEE Conference on Decision and Control, 16–18. https://doi.org/10.1109/CDC.1992.371232
Baric, M., Baotic, M., Morari, M., 2005. On-line Tuning of Controllers for Systems with Constraints. IEEE Conference on Decision and Control and European Control Conference (4), 8288–8293.
Bouskova, A., Dohanyos, M., Schmidt, J., 2005. Strategies for changing temperature from mesophilic to thermophilic conditions in anaerobic CSTR reactors treating sewage sludge. Water Research 39 (8), 1481–1488. https://doi.org/10.1016/j.watres.2004.12.042
Camp, C., Barron, J., 2004. Design of Space Trusses Using Ant Colony Optimization. Journal of Structural Engineering 130 (5), 741–751. https://doi.org/10.1061/(ASCE)0733-9445(2004)130:5(741)
Chen, Hong, A, K., 1995. Nonlinear Predictive Control of a Benchmark CSTR. In: European Control Conference ECC. No. April. pp. 3247–3252.
Cohen, G., Coon, G., 1953. Theoretical considerations of retarded control. Transactions of the ASME 75, 827–834.
Domingues, D. S., Takahashi, H. W., Camara, C. A. P., Nixdorf, S. L., 2012. Automated system developed to control pH and concentration of nutrient solution evaluated in hydroponic lettuce production. Computers and Electronics in Agriculture 84, 53–61. https://doi.org/10.1016/j.compag.2012.02.006
Dougherty, D., Cooper, D. J., 2003. A practical multiple model adaptive strategy for multivariable model predictive control. Engenineering practice 11, 649–664.
Ebenau, G., Rottschafer, J., 2005. An advanced evolutionary strategy with an adaptive penalty function for mixed-discrete structural optimisation. Advances in Engineering Software 36 (1), 29–38. https://doi.org/10.1016/j.advengsoft.2003.10.008
Ge, S., Hang, C., Zhang, T., 1999. Nonlinear adaptive control using neural networks and its application to CSTR systems. Journal of Process Control 9 (4), 313–323. https://doi.org/10.1016/S0959-1524(98)00054-7
Genceli, H., Nikolaou, M., 1993. Robust Stability Analysis of Constrained /,-Norm Model Predictive Control. AIChE 39 (12).
Gholaminejad, T., Khaki-Sedigh, A., Bagheri, P., jan 2016. Adaptive tuning of model predictive control based on analytical results. In: 2016 4th International Conference on Control, Instrumentation, and Automation (ICCIA). IEEE, pp. 226–232. https://doi.org/10.1109/ICCIAutom.2016.7483165
Guerra, J., 2008. Analisis dinamico de un CSTR Laboratorio de Dinamica y Control. Tech. rep., Universidad Iberoamericana, Ciudad de Mexico.
Herrera, J., Ibeas, A., de la Sen, M., Alcantara, M., Serna-Garces, S., 2013. Identification and control of delayed siso systems through Pattern Search Methods. J. Franklin Inst. B, 3128–3148.
Hinde, R., Cooper, D. J., 1994. A Pattern-based Approach to Excitation Diagnostics for Adaptive Process Control. Chem. Eng. Sci 49 (9), 1403–1415. https://doi.org/10.1016/0009-2509(94)85069-0
Isaza, J. A., 2012. Evaluación de un controlador predictivo basado en un modelo semifísico de inferencia borrosa Takagi-Sugeno con conjuntos multidimensionales Evaluation of a model predictive control using a fuzzy inference system Takagi- Sugeno with multidimensional sets. Ph.D. thesis.
Lemonge, A., Barbosa, H., 2003. A new adaptive penalty scheme for genetic algorithms. Information Sciences 3 (156), 215–251.
Morari, M., Lee, J. H., 1999. Model predictive control: past, present and future. Computers & Chemical Engineering 23, 667–682. https://doi.org/10.1016/S0098-1354(98)00301-9
Nagrath, D., Prasad, V., Bequette, B.W., 2002. A model predictive formulation for control of open-loop unstable cascade systems. Chemical Engineering Science 57 (3), 365–378. https://doi.org/10.1016/S0009-2509(01)00398-0
Norapat, N., Bureerat, S., 2011. Simultaneous topology, shape and sizing optimization of a three-dimensional slender truss tower using multiobjective evolutionary algorithms. Computers n& Structures 89, 2531 – 2538.
Rawlings, J. B., Muske, K. R., 1993. Stability of constrained receding horizon control. IEEE Transactions on Automatic Control 38 (10), 1512–1516. https://doi.org/10.1109/9.241565
Schutte, J., Groenwold, A., 2003. Sizing design of truss structures using particle swarms. Structural and Multidisciplinary Optimization 25, 261–269. https://doi.org/10.1007/s00158-003-0316-5
Shabani, R., Sedigh, A. K., Salahshoor, K., 2010. Robust Control of a pH Neutralization Process Plant Using QFT. IEEE Xplore 2, 594–598.
Shridhar, R., Cooper, D. J., 1998. A Tuning Strategy for Unconstrained Multivariable Model Predictive Control. Industrial & engineering chemistry research 5885 (98), 4003–4016.
https://doi.org/10.1021/ie980202s
Srinivasarao, P., Subbaiah, P., 2014. Tuning of Nonlinear Model Predictive for qwuadruple tank process. journal of theoretical and applied information technology 67 (2), 316–326.
Suzuki, R., Kawai, F., Ito, H., Nakazawa, C., Fukuyama, Y., Aiyoshi, E., 2007. Automatic Tuning of Model Predictive Control Using Particle Swarm Optimization. In: 2007 IEEE Swarm Intelligence Symposium. No. Sis. pp. 1–6. https://doi.org/10.1109/SIS.2007.367941
Syafiie, S., Tadeo, F., Martinez, E., Dec 2009. Q( x03bb;) learning technique for ph control. In: 2009 IEEE International Conference on Industrial Engineering and Engineering Management. pp. 712–716. https://doi.org/10.1109/IEEM.2009.5373232
Takagi, T., Sugeno, M., jan 1985. Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man, and Cybernetics SMC-15 (1), 116–132. https://doi.org/10.1109/TSMC.1985.6313399
Trierweiler, J., Farina, L., oct 2003. RPN tuning strategy for model predictive control. Journal of Process Control 13 (7), 591–598. https://doi.org/10.1016/S0959-1524(02)00093-8
Wojsznis, W., Gudaz, J., Blevins, T., Mehta, A., 2003. Practical approach to tuning MPC *. ISA transactions 42, 149–162. https://doi.org/10.1016/S0019-0578(07)60121-9
Yamuna, K., Unbehauen, H., 1997. Study of Predictive Controller Tuning Methods * IL. Automatica 33 (12), 2243–2248. https://doi.org/10.1016/S0005-1098(97)00134-9
Ziegler, J., Nichols, N., 1942. Optimum settings for automatic controllers. Transactions of the ASME, 759–768.
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