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.
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