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

Author:
Morales Alvarado, Christiam Segundo, Garcia, Claudio
Source:
Industrial & engineering chemistry process design and development 2018 v.57 no.10 pp. 3666-3677
ISSN:
1520-5045
Subject:
algorithms, controllers, dynamic models, industrial applications, linear models, model validation, neutralization, pH, process control, process design
Abstract:
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.
Agid:
6019510