Online optimal control of a batch fermentation process using multiple model approach

Azimzadeh, F.; Palizban, H.A.; Romagnoli, JA

doi:10.1109/cdc.1998.760718

Cited by 4 publications

(2 citation statements)

References 3 publications

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“…Some approches use space state models like (Azimadeh et al, 1998) and (Foss et al, 1995). In (Azimadeh et al, 1998) a set of linear space state models is chosen in a given trajectory.…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Model Approach for Bilinear Generalized Predictive Control

Cavalcanti¹,

Maitelli²,

Fontes³

2007

Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics

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This paper presents a contribution in multivariable predictive control. A new approach of multi-model based control is presented. The controller used is the quasilinear multivariable generalized predictive control (QMGPC). A metric based in 2-norm is presented in order to build a global model using local models. Simulation results in a distillation column, with a comparative analysis, are presented.

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“…Some approches use space state models like (Azimadeh et al, 1998) and (Foss et al, 1995). In (Azimadeh et al, 1998) a set of linear space state models is chosen in a given trajectory.…”

Section: Introductionmentioning

confidence: 99%

“…Some approches use space state models like (Azimadeh et al, 1998) and (Foss et al, 1995). In (Azimadeh et al, 1998) a set of linear space state models is chosen in a given trajectory. In (Foss et al, 1995) a set on nonlinear space state models is chosen (and a nonlinear predictive controller is designed).…”

Section: Introductionmentioning

confidence: 99%

A Multi-Model Approach for Bilinear Generalized Predictive Control

Cavalcanti¹,

Maitelli²,

Fontes³

2007

Proceedings of the Fourth International Conference on Informatics in Control, Automation and Robotics

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Model predictive control of pH neutralization processes: A review

Hermansson

Syafiie

2015

Control Engineering Practice

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The paper provides a review of the different approaches of Model Predictive Control (MPC) to deal with the nonlinearities and transient behavior associated with pH and its control. Firstly a description of the pH system and what makes it difficult to control is presented, followed by the general description of the structure of MPC. The different applications of MPC vary mostly in the way the model is described and how the optimization is carried out to obtain the desired control action. The different modeling techniques applied to the MPC, which is used to describe the behavior of the pH are ranging from simple linear models, multiple linear models like piecewise linear descriptions and fuzzy models, to nonlinear descriptions like Wiener models and the use of artificial neural networks. The models and their respective ways of application are reviewed. Finally, the areas where more research is needed are addressed.

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