Assessment of Model Predictive Control Performance Criteria

作     者：Rafael Lopes Duarte-Barros Song Won Park 

作者机构：Emerson Process Management Rio de Janeiro 20. 090-003 Brazil Department of Chemical Engineering University of Sao Paulo Sao Paulo 05424-970 Brazil 

出 版 物：《Journal of Chemistry and Chemical Engineering》 (化学与化工（英文版）)

年 卷 期：2015年第9卷第2期

页      面：127-135页

学科分类：08[工学] 0835[工学-软件工程] 0802[工学-机械工程] 080201[工学-机械制造及其自动化] 

主　　题：Predictive controller performance minimum variance capability MPC GPC ESSMPC (extended state space model predictive controller). 

摘      要：The current highly competitive environment has driven industries to operate with increasingly restricted profit margins. Thus, it is imperative to optimize production processes. Faced with this scenario, multivariable predictive control of processes has been presented as a powerful alternative to achieve these goals. Moreover, the rationale for implementation of advanced control and subsequent analysis of its post-match performance also focus on the benefits that this tool brings to the plant. It is therefore essential to establish a methodology for analysis, based on clear and measurable criteria. Currently, there are different methodologies available in the market to assist with such analysis. These tools can have a quantitative or qualitative focus. The aim of this study is to evaluate three of the best current main performance assessment technologies： Minimum Variance Control-Harris Index; Statistical Process Control （Cp and Cpk）; and the Qin and Yu Index. These indexes were studied for an alumina plant controlled by three MPC （model predictive control） algorithms （GPC （generalized predictive control）, RMPCT （robust multivariable predictive control technology） and ESSMPC （extended state space model predictive controller）） with different results.