14th IFAC Symposium on System Identification, SYSID 2006

SYSID-2006 Paper Abstract

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Paper FrA3.5

Zhao, Hong (Aspen Tech. Inc.), Harmse, Michael (Aspen Tech. Inc.), Guiver, John (Aspen Tech. Inc.), Canney, William (Aspen Tech. Inc.)

Subspace Identification in Industrial MPC Applications - a Review of Recent Progress and Industrial Experience

Scheduled for presentation during the Invited Session "New Developments in Closed-Loop Subspace Identification" (FrA3), Friday, March 31, 2006, 11:50−12:10, Hunter Room

14th IFAC Symposium on System Identification, March 29 - 31, 2006, Newcastle, Australia

This information is tentative and subject to change. Compiled on September 24, 2018

Keywords Subspace Methods, Closed Loop Identification, Process Control

Abstract

The subspace identification has been available in industrial advanced process control (APC) applications in a commercial APC package since 2000. After five years of industrial use, several issues were identified and addressed; the subspace identification technology has been widely accepted and is now used in numerous industrial APC projects. In this paper, a review of the 5-year application history with industrial experience and recent progress in the plant test and identification is given. Questions like why the parametric model identification (ID) methods, which have been theoretically proved optimal, such as prediction error approach, are not routinely used by industrial APC practitioners and why the APC practitioners are now in favour of subspace ID are answered from a point of view of industrial practice. Several important practical ID issues that are more concerned by industrial practitioners are discussed. More specifically, a new view on the issue of open-loop vs. closed-loop ID is provided with the recent progress in multi-variable constrained plant testing technology. It has been found that the subspace identification works very well with the innovative plant testing approach in a synergistic way, and able to substantially reduce plant test duration. As a result, improved identification efficiency from shorter but richer data sets results in better model accuracy, and consequently project costs have been reduced significantly over the past 4 years. With more applications of the subspace ID in industrial MPC projects, some known issues and the future needs are also provided to invite academic researchers to help address.