Model Predictive Control in Industry: Challenges and Opportunities

被引：196

作者：

Forbes, Michael G. ^{[1
]}

Patwardhan, Rohit S. ^{[2
]}

Hamadah, Hamza ^{[2
]}

Gopaluni, R. Bhushan ^{[3
]}

机构：

[1] Honeywell Proc Solut, N Vancouver, BC V7J 3S4, Canada

[2] Saudi Aramco, Proc & Control Syst Dept, Dhahran 31311, Saudi Arabia

[3] Univ British Columbia, Dept Chem & Biol Engn, Vancouver, BC V6T 1Z3, Canada

来源：

IFAC PAPERSONLINE | 2015年 / 48卷 / 08期

关键词：

industrial control; process control; model-based control; predictive control; adaptive control; performance monitoring; control applications; human factors; MPC;

D O I：

10.1016/j.ifacol.2015.09.022

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

With decades of successful application of model predictive control (MPC) to industrial processes, practitioners are now focused on ease of commissioning, monitoring, and automation of maintenance. Many industries do not necessarily need better algorithms, but rather improved usability of existing technologies to allow a limited workforce of varying expertise to easily commission, use, and maintain these valued applications. Continuous performance monitoring, and automated model re identification are being used as vendors work to deliver automated adaptive MPC. This paper examines industrial practice and emerging research trends towards providing sustained MPC performance. (C) 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

引用

页码：531 / 538

页数：8

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