fevereiro 2015 vol. 1 num. 2 - XX Congresso Brasileiro de Engenharia Química

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A COMPARISON OF PERFORMANCE AND IMPLEMENTATION CHARACTERISTICS OF NMPC FORMULATIONS

GONÇALVES, G. A. A.; ALVARISTO, E. L.; SILVA, G. C.; SOUZA JR, M. B.; SECCHI, A. R.;

Artigo:

Three nonlinear model predictive control (NMPC) strategies are compared on the control of the isothermal CSTR (continuous stirred tank reactor) with van der Vusse kinetics, which is largely employed in control studies. This reactor exhibits sign change of the process static gain and nonminimum phase dynamic behavior. The first strategy considers a NMPC coupled with a state estimator. The second one uses neural networks as the internal NMPC multivariable model. In the last one, a proposed approach for the adaptation of the linear MPC (model predictive control) to nonlinear systems is employed in order to generate predictions through successive local linearizations around steady states. The results show that the NMPC with state estimation stabilized the system at the expense of higher computational cost. The strategy based on neural networks demanded a shorter time for the calculation of the control actions, allowing the use of a shorter sampling time. The adaptive MPC stabilizes the nonlinear system around points which are unstable under linear MPC control, demanding less computational effort than the NMPC with a state estimator.

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DOI: 10.5151/chemeng-cobeq2014-0789-23867-164120

Referências bibliográficas
  • [1] ALLGÖWER, F.; FINDEISEN, R.; NAGY, Z. K. Nonlinear model predictive control from theory to application. J Chin. Inst. Chem. Engrs, v. 35, n. 3, p. 299-315, 2004.
  • [2] AKPAN, V. A.; HASSAPIS, G. D., Nonlinear model identification and adaptive model predictive control using neural networks. ISA transactions, v. 50, n. 2 , p. 177–94, 2011.
  • [3] BENAMOR, S.; DOYLE, F. J.; MCFARLANE, R. Polymer grade transition control using advanced real-time optimization software. Journal of Process Control, v. 14, p. 349-364, 2004.
  • [4] BIEGLER, L. T.; HUGHES, R. R., Feasible path optimization with sequential modular simulators, Computers Andamp; Chemical Engineering, v. 9, n. 4, p. 379–394,1985.
  • [5] CERVANTES, A. M.; TONELLI, S.; BRANDOLIN, A.; BANDONI, J. A.; BIEGLER, L. T. Large-Área temática: Simulação, Otimização e Controle de Processos 7scale dynamic optimization for grade transitions in a low density polyethylene plant. Computers and Chemical Engineering, v. 26, p. 227-237, 2002.
  • [6] DE OLIVEIRA, L. B.; CAMPONOGARA, E. Multi-agent model predictive control of signaling split in urban traffic networks. Transportation Research Part C - Emerging Technologies, v. 18, p. 120-139, 2010.
  • [7] DURAISKI, R. G. Controle Preditivo Não Linear Utilizando Linearizações ao Longo da Trajetória. Universidade Federal do Rio Grande do Sul. M.Sc. dissertation. 2001 (in Portuguese). GLAVIC, P.; BUTINAR, B.; BIKIC, D. Optimal reactor systems for Van de Vusse reaction scheme with multicomponent feed. Computers Andamp; Chemical Engineering, v. 26, p. 1335-1343, 2002.
  • [8] JADBABAIE, A.; HAUSER, J., On the stability of unconstrained receding horizon control with a general terminal cost, Decision and Control, 2001. Proceedings of the 40th IEEE Conference on , pp.4826,4831 vol.5, 2001 KEERTHI, S., GILBERT, E. Optimal infinite horizon feedback laws for a general class of constrained discrete-time systems: stability and moving horizon approximations, Journal of Optimization Theory and Applications, 57, p. 265-293, 198
  • [9] LIMON,D.; ALAMO, T.; SALAS, F.; CAMACHO, E. F. On the stability of MPC without terminal constraint. IEEE Trans. Auto. Cont., v. 51, n. 5, p.832-836, 2006.
  • [10] LOPEZ-NEGRETE, R. D’A.; BIEGLER, L. T.; KUMAR, A. Fast nonlinear model predictive control: Formulation and industrial process applications, Computers Andamp; Chemical Engineering, v.51, p.55-64, 2013.
  • [11] MANENTI, F. Considerations on nonlinear model predictive control techniques.Computers and Chemical Engineering, v. 35, p. 2491-2509, 20
  • [12] MATHWORKS, MATLAB, version 7.8.0 (R2008a). Natick, Massachusetts, TheMathWorksInc., 2008.
  • [13] MAYNE, D. Q.; RAWLINGS, J. B.; RAO, C. V.; SCOKAERT, P. O. M. Constrained model predictive control : Stability and optimality, Automatica, v. 36, p. 789–814, 2000.
  • [14] QIN, S. J.; BADGWELL, T. A.An Overview of Nonlinear Model Predictive Control Applications.Progress in Systems as ControlTheory, Switzerland, v. 26, p. 369-392, 2000.
  • [15] RAWLINGS, J. B., MAYNE, D. Q. Model Predictive control: Theory and Design. Madison: Nob Hill Publishing, 2009.
  • [16] SALAHSHOOR, K.; ZAKERI, S.; HAGHIGHAT SEFAT, M., Stabilization of gas-lift oil wells by a nonlinear model predictive control scheme based on adaptive neural network models, Engineering Applications of Artificial Intelligence, v. 26, n. 8 (set.), pp. 1902–1910, 2013.
  • [17] SECCHI, A.R.. “DASSLC v 3.8: Differential-Algebraic System Solver in C”. http://www.enq.ufrgs.br/enqlib/numeric, 2012.
  • [18] TRIERWEILER, J. O. A Systematic Approach to Control Structure Design.D.Sc. Thesis,UniversitätDortmund. Germany. 1997.
Como citar:

GONÇALVES, G. A. A.; ALVARISTO, E. L.; SILVA, G. C.; SOUZA JR, M. B.; SECCHI, A. R.; "A COMPARISON OF PERFORMANCE AND IMPLEMENTATION CHARACTERISTICS OF NMPC FORMULATIONS", p. 11746-11753 . In: Anais do XX Congresso Brasileiro de Engenharia Química - COBEQ 2014 [= Blucher Chemical Engineering Proceedings, v.1, n.2]. São Paulo: Blucher, 2015.
ISSN 2359-1757, DOI 10.5151/chemeng-cobeq2014-0789-23867-164120

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