Linear Parameter-varying Models For Predictive Control Design: Application To Nonlinear Chemical Reactors Thiago Vaz Da Costa

This paper presents the application of an identification algorithm based on local model networks able to split the full model dynamics in linear parameter-varying (LPV) models for different regions on the process operating range. It is shown that a model based controller equipped with an efficient LPV model performs better than when a single linear time-invariant (LTI) model is used. Results demonstrated that model adaptation over several regions provides better system representation leading to more efficient and consistent control in already implemented control loops.212219Ozkan, L., Kothare, M.V., Georgakis, C., Control of a solution copolymerization reactor using multi-model predictive control (2003) Chemical Engineering Science, 58 (7), pp. 1207-1221. , DOI 10.1016/S0009-2509(02)00559-6, PII S0009250902005596Babuska, R., Verbruggen, H., Neuro-fuzzy methods for nonlinear system identification (2003) Annual Reviews in Control, 27 (1), pp. 73-85Causa, J., Karer, G., Núñez, A., Sáez, D., Škrjanc, I., Zupančič, B., Hybrid fuzzy predictive control based on genetic algorithms for the temperature control of a batch reactor (2008) Computers and Chemical Engineering, 32 (12), pp. 3254-3263Ljung, L., (1999) System Identification: Theory for the User, , 2nd ed., Prentice Hall, New JerseyNelles, O., (2001) Nonlinear System Identification: From Classical Approaches to Neural Networks and Fuzzy Models, , Berlin, SpringerHafner, M., Schüler, M., Nelles, O., Isermann, R., Fast neural networks for diesel engine control design (2000) Control Engineering Practice, 8 (11), pp. 1211-1221Nelles, O., Isermann, R., Basis function networks for interpolation of local linear models (1996) Proc. of the 35th IEEE Conference on Decision & Control, pp. 470-475. , Kobe, JapanRossiter, J., (2003) Model-based Predictive Control: A Practical Approach, , Florida, CRC PressFischer, M., Nelles, O., Isermann, R., Predictive control based on local linear fuzzy models (1998) International Journal of Systems Science, 29 (7), pp. 679-697Espinosa, J., Vandewalle, J., Predictive control using fuzzy models (1998) Advances in Soft Computing: Engineering Design and Manufacturing, 8 (11), pp. 187-200. , Springer-VerlagBlazic, S., Skrjanc, I., Design and stability analysis of fuzzy model-based predictive control - A case study (2007) Journal of Intelligent and Robotic Systems: Theory and Applications, 49 (3), pp. 279-292. , DOI 10.1007/s10846-007-9147-8Khairy, M., Elshafei, A., Emara, H., LMI based design of constrained fuzzy predictive control (2010) Fuzzy Sets and Systems, 161 (6), pp. 893-918Bequette, B.W., Non-linear model predictive control: A personal retrospective (2007) Canadian Journal of Chemical Engineering, 85 (4), pp. 408-415Boling, J.M., Seborg, D.E., Hespanha, J.P., Multi-model adaptive control of a simulated pH neutralization process (2007) Control Engineering Practice, 15 (6), pp. 663-672. , DOI 10.1016/j.conengprac.2006.11.008, PII S0967066106002188, Special Section on Control Applications in Marine SystemsMahmoodi, S., Poshtan, J., Jahed-Motlagh, M., Montazeri, A., Nonlinear model predictive control of a pH neutralization process based on wiener-laguerre model (2009) Chemical Engineering Journal, 146 (3), pp. 328-337Marusak, P., Advantages of an easy to design fuzzy predictive algorithm in control systems of nonlinear chemical reactors (2009) Applied Soft Computing, 9 (3), pp. 1111-1125Henson, M., Seborg, D., Adaptive nonlinear control of a pH neutralization process (1994) IEEE Transactions on Control Systems Technology, 2 (3), pp. 169-182Gustafsson Tore, K., Waller Kurt, V., Dynamic modeling and reaction invariant control of pH (1983) Chemical Engineering Science, 38 (3), pp. 389-398. , DOI 10.1016/0009-2509(83)80157-