Application of Robust Model Predictive Control Using Principal Component Analysis to an Industrial Thickener

被引：0

作者：

Jia, Runda ^{[1
]}

You, Fengqi ^{[2
]}

机构：

[1] Northeastern Univ, Sch Informat Sci & Engn, Shenyang 110004, Peoples R China

[2] Cornell Univ, Robert Frederick Smith Sch Chem & Biomol Engn, Ithaca, NY 14853 USA

来源：

IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY | 2024年 / 32卷 / 03期

关键词：

Pressure sensors; Slurries; Linear systems; Safety; Mathematical models; Principal component analysis; Predictive models; Data-driven robust optimization; model predictive control (MPC); principal component analysis (PCA); thickener; OPTIMIZATION;

D O I：

10.1109/TCST.2024.3355012

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A data-driven robust model predictive control (DRMPC) method is used in this brief to control an industrial thickener. To estimate the future states of the thickener, a discrete-time linear time-invariant (LTI) model is employed using the data information from the pressure sensors installed inside the thickener. The hard constraints on the amount of dry ores in the thickener and the underflow concentration are considered in the thickening process, and a robust model predictive control (RMPC) framework based on the affine disturbance feedback (ADF) technique is developed. To better describe the prediction errors of the LTI model of the thickener, principal component analysis (PCA) is employed to properly construct the uncertainty set from the historical data. Case studies based on a simulation platform and an industrial thickener are presented to evaluate the efficiency of the method.

引用

页码：1090 / 1097

页数：8

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