A COMPARISON OF PERFORMANCE AND IMPLEMENTATION CHARACTERISTICS OF NMPC FORMULATIONS

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.

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

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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 23591757, DOI 10.5151/chemeng-cobeq2014-0789-23867-164120