APPLICATION OF AN OPTIMAL MPC TUNING STRATEGY IN CONTROL OF A NON LINEAR REACTOR SYSTEM

APPLICATION OF AN OPTIMAL MPC TUNING STRATEGY IN CONTROL OF A NON LINEAR REACTOR SYSTEM

FONTES, R. M.; MARTINS, M. A. F.; KALID, R. A.

Artigo:

This paper concerns the use of a particle swarmoptimization-based MPC tuning method so as to compare the performancesbetween the conventional MPC and an infinite horizon MPC, when both areapplied to a reactor system. More specifically, the tuning method is carried outon a simulated CSTR system using linearized models along with process/modelmismatch, and so the optimal tuning parameters are also applied to theCSTR nonlinear model. The simulated results show that the infinite horizonMPC remains stable in all simulated scenarios, whereas the conventional MPCdestabilizes when the nonlinear system is required to be controlled.

This paper concerns the use of a particle swarmoptimization-based MPC tuning method so as to compare the performancesbetween the conventional MPC and an infinite horizon MPC, when both areapplied to a reactor system. More specifically, the tuning method is carried outon a simulated CSTR system using linearized models along with process/modelmismatch, and so the optimal tuning parameters are also applied to theCSTR nonlinear model. The simulated results show that the infinite horizonMPC remains stable in all simulated scenarios, whereas the conventional MPCdestabilizes when the nonlinear system is required to be controlled.

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DOI: 10.5151/chemeng-cobeq2014-1373-19592-177098

Referências bibliográficas

• [1] Christofides, P. D.; Scattolini, R.; Mun˜oz de la Pen˜a, D.; Liu, J. Distributedmodel predictive control: A tutorial review and future research directions. ComputersAndamp; Chemical Engineering, 51, 21–41, 2013.
• [2] Darby, M. L.; Nikolaou, M. MPC: Current practice and challenges. ControlEngineering Practice, 20(4), 328–342, 2012.
• [3] Garc´ıa, C. E.; Prett, D. M.; Morari, M. Model Predictive Control: Theory andPractice a Survey. Automatica, 25(3), 335–348, 1989.
• [4] Garriga, J. L.; Soroush, M. Model Predictive Control Tuning Methods: A Review.Industrial Andamp; Engineering Chemistry Research, 49(8), 3505–3515, 2010.
• [5] Gonza´lez, A. H.; Adam, E.; Marchetti, J. Conditions for offset elimination instate space receding horizon controllers: A tutorial analysis. Chemical Engineering andProcessing: Process Intensification, 47(12), 2184–2194, 2008.
• [6] Lee, J. H.; Yu, Z. H. Tuning of model predictive controllers for robust performance.Computers Chemical Engineering, 18(1), 15–37, 1994.
• [7] Lee, J. H. V. D.; Svrcek, W. Y.; Young, B. R. A tuning algorithm for modelpredictive controllers based on genetic algorithms and fuzzy decision making. ISAtransactions, 47, 53–59, 2008.
• [8] Maciejowski, J. M. Predictive Control with Constraints. Prentice Hall, 2000.
• [9] Nery Ju´nior, G. A.; Marins, M. A. F.; Kalid, R. A PSO-based optimal tuningstrategy for constrained multivariable predictive controllers with model uncertainty.ISA transactions, 53(2), 560–7, 2014.
• [10] 7Área temática: Simulação, Otimização e Controle de Processos 7Odloak, D. Extended robust model predictive control. AIChE Journal, 50(8),1824–1836, 2004.
• [11] Rawlings, J. B.; Muske, K. R. The stability of constrained receding horizon control.IEEE Transactions on Automatic Control, 38(10), 1512–1516, 1993.
• [12] Shridhar, R.; Cooper, D. J. A novel tuning strategy for multivariable modelpredictive control. ISA transactions, 36(4), 237–280, 1998a.Shridhar, R.; Cooper, D. J. A Tuning Strategy for Unconstrained MultivariableModel Predictive Control. Industrial Andamp; Engineering Chemistry Research, 37(98),4003–4016, 1998b.Susuki, R.; Kawai, F.; Nakazawa, C.; Matsui, T.; Aiyoshi, E. Parameteroptimization of model predictive control using PSO. 2008 SICE Annual Conference, p.1981–1988, 2008.
• [13] 8

Como citar:

FONTES, R. M.; MARTINS, M. A. F.; KALID, R. A.; "APPLICATION OF AN OPTIMAL MPC TUNING STRATEGY IN CONTROL OF A NON LINEAR REACTOR SYSTEM", p-12391-12398. In: Anais do XX Congresso Brasileiro de Engenharia Química - COBEQ 2014 [= Blucher Chemical Engineering Proceedings, v.1, n.2]. São Paulo: Blucher, 2015.
ISSN 23591757, DOI 10.5151/chemeng-cobeq2014-1373-19592-177098