Algorithms and Methods for Identification of Multivariable Plants

Garcia, Cláudio; Potts, Alain Segundo; Godoy, Rodrigo Juliani Correa de; Alves, Vitor Alex Oliveira; Silva, Tiago Sanches da

doi:10.1016/b978-0-444-63456-6.50112-5

Cited by 3 publications

(1 citation statement)

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“…To obtain dynamic models of industrial processes, system identification techniques are applied using the data collected under certain operating conditions of the process . In the last 20 years, system identification techniques have provided reliable alternatives to obtain mathematical models, applied to different chemical processes. − Thus, system identification allows building model-based control systems, considering that the identified model is a reliable description of the plant behavior around the operating point of the process.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Statistical Metrics and a New Fuzzy Method for Validating Linear Models Used in Model Predictive Control Controllers

Alvarado¹,

Garcia

2018

Ind. Eng. Chem. Res.

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In the advanced process control area, model predictive control (MPC) implementations have been successful in many industrial applications. Despite being an optimization-based control technique, sometimes problems occur with the control algorithm when the dynamic model is not adequate. This work compares statistical techniques for model validation to quantify the quality of identified models used in multivariable MPC controllers. Additionally, a fuzzy validation system is proposed, showing the consistency between the model validation and the predictive controller performance. Multivariable identification, model validation, and predictive controller implementation are performed in an industrial-scale pH neutralization pilot plant.

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