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

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.

Multivariable System Identification Method Based on Continuous Action Reinforcement Learning Automata

“…Considering that the presented CARLA-FRE is applicable for a variety of excitation signals, the parameters of the decomposed equivalent SISO system can be estimated using CARLA-FRE. Then, the identified SISO can be combined into a MIMO system according to Formulas ( 24) and (19) to complete the multivariable system identification.…”

“…It is often affected by external disturbance and the change of internal working conditions. Therefore, authors in [16][17][18][19] proposed frequency response estimation methods for the identification of the closed-loop system. However, the existing methods still have some limitations (e.g., the accuracy depends on the choice of attenuation factor).…”