Abstract
A fuzzy model with reduced complexity has been developed to capture the nonlinear dynamics of a pilot plant in which the temperature of a reactor is controlled. The use of Functional Principal Component Analysis provides an ability to reduce the complexity of the model permitting the application of linear MPC for the nonlinear control problem.
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References
Camacho, E., Bordons, C.: Model predictive control. In: Advanced Textbooks in Control and Signal Processing. Springer (2007)
Qin, S., Badgwell, T.: An overview of nonlinear model predictive control applications. In: Allgöwer, F., Zheng, A. (eds.) Nonlinear Model Predictive Control. Progress in Systems and Control Theory, Birkhäuser Basel, vol. 26, pp. 369–392 (2000). http://dx.doi.org/10.1007/978-3-0348-8407-5_21
Potocnik, B., Music, G., Zupancic, B.: Model predictive control systems with discrete inputs. In: Proceedings of the 12th IEEE Mediterranean Electrotechnical Conference. Melecon 2004, vol. 1, pp. 383–386, May 2004
Karer, G., Mušič, G., Škrjanc, I., Zupančič, B.: Hybrid fuzzy model-based predictive control of temperature in a batch reactor. Comput. Chem. Eng. 31(12), 1552–1564 (2007). http://www.sciencedirect.com/science/article/pii/S0098135407000051
Núñez, A., Sáez, D., Oblak, S., Škrjanc, I.: Fuzzy-model-based hybrid predictive control. ISA Trans. 48(1), 24–31 (2009). http://www.sciencedirect.com/science/article/pii/S0019057808000682
Babuska, R., Sousa, J., Verbruggen, H.: Predictive control of nonlinear systems based on fuzzy and neural models. In: European Control Conference, p. 667 (1999)
Marusak, P., Tatjewski, P.: Stability analysis of nonlinear control systems with unconstrained fuzzy predictive controllers. Arch. Control Sci. 12(3), 267–288 (2002)
Tatjewski, P.: Advanced control of industrial processes: structures and algorithms. In: Advances In Industrial Control. Springer (2007)
Huang, Y.L., Lou, H.H., Gong, J.P., Edgar, T.F.: Fuzzy model predictive control. IEEE Trans. Fuzzy Syst. 8(6), 665–678 (2000)
Mahalanabis, A.K.: Large scale systems modelling and control, m. jamshidi, north holland, 1983. no. of pages: 524. price: 34.10. Optimal Control Applications and Methods 5(4), 367–367 (1984). doi:10.1002/oca.4660050410
Escaño. J., Bordons, C.: Neurofuzzy model of an industrial processs, reducing complexity by using principal component analysis. In: XVI Congreso Español sobre Tecnologías y Lógica Fuzzy (ESTYLF 2012) (2012)
Gruber, J., Bordons, C., Bars, R., Haber, R.: Nonlinear predictive control of smooth nonlinear systems based on volterra models. application to a pilot plant. Int. J. Robust Nonlinear Control 20(16), 1817–1835 (2010). doi:10.1002/rnc.1549
Ramírez, D., Gruber, J., Álamo, T., Bordóns, C., Camacho, E.: Control predictivo mín-máx de una planta piloto. Revista Iberoamericana de Automática e Informática Industrial RIAI 5(3), 37–47 (2008). http://www.sciencedirect.com/science/article/pii/S1697791208701602
Chiu, S.: Fuzzy Model Identification based on cluster estimation. J. Intell. Fuzzy Syst. 2, 267–278 (1994)
Fuzzy Logic Toolbox User’s Guide. COPYRIGHT 1995–2017 by The MathWorks, Inc. Revised for Version 2.2.25 (Release 2017a) (March 2017). https://www.mathworks.com/help/pdf_doc/fuzzy/fuzzy.pdf
Jang, J.: Anfis: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)
Escaño, J., Bordons, C.: Complexity reduction in fuzzy systems using functional principal component analysis. In: Matía, F., Marichal, G.N., Jiménez, E. (eds.) Fuzzy Modeling and Control: Theory and Applications. Atlantis Computational Intelligence Systems, Atlantis Press, vol. 9, pp. 49–65 (2014). http://dx.doi.org/10.2991/978-94-6239-082-9_3
Deville, J.: Méthodes statistiques et numériques de l’analyse harmonique. Annales de l’inséé. 15, 3, 5–101 (1974)
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Escaño, J.M., Witheephanich, K., Bordons, C. (2018). Fuzzy Model Based Predictive Control of Reaction Temperature in a Pilot Plant. In: Kacprzyk, J., Szmidt, E., Zadrożny, S., Atanassov, K., Krawczak, M. (eds) Advances in Fuzzy Logic and Technology 2017. EUSFLAT IWIFSGN 2017 2017. Advances in Intelligent Systems and Computing, vol 642. Springer, Cham. https://doi.org/10.1007/978-3-319-66824-6_1
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DOI: https://doi.org/10.1007/978-3-319-66824-6_1
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