Stochastic Model Predictive Control With Closed-Loop Model Updating

被引：0

作者：

Santander, Omar ^{[1
]}

Baldea, Michael ^{[1
,3
]}

Soderstrom, Tyler A. ^{[2
]}

机构：

[1] Univ Texas Austin, McKetta Dept Chem Engn, Austin, TX 78712 USA

[2] ExxonMobil Technol & Engn, Houston, TX 77389 USA

[3] Univ Texas Austin, Oden Inst Computat Engn & Sci, Austin, TX 78712 USA

来源：

INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH | 2023年 / 62卷 / 40期

关键词：

PERFORMANCE ASSESSMENT; MISMATCH ESTIMATION; IDENTIFICATION; BATCH;

D O I：

10.1021/acs.iecr.3c01835

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Updating the process model remains an important concern in practical implementations of Model Predictive Control (MPC). This work introduces a novel stochastic model predictive control (MPC) framework with closed-loop model updating based on closed-loop operating data. The proposed framework is tested on a canonical chemical process case study. Simulations demonstrate that the economic performance is similar to a conventional MPC, but the model prediction accuracy and controller error are substantially reduced under all disturbance and operational scenarios considered.

引用

页码：16344 / 16359

页数：16

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