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A data-based approach for multivariate model predictive control performance monitoring

A data-based approach for multivariate model predictive control performance monitoring
A data-based approach for multivariate model predictive control performance monitoring
An intelligent statistical approach is proposed for monitoring the performance of multivariate model predictive control (MPC) controller, which systematically integrates both the assessment and diagnosis procedures. Model predictive error is included into the monitored variable set and a 2-norm based covariance benchmark is presented. By comparing the data of a monitored operational period with the "golden" user-predefined one, this method can properly evaluate the performance of an MPC controller at the monitored operational stage. Characteristic direction information is mined from the operating data and the corresponding classes are built. The eigenvector angle is defined to describe the similarity between the current data set and the established classes, and an angle-based classifier is introduced to identify the root cause of MPC performance degradation when a poor performance is detected. The effectiveness of the proposed methodology is demonstrated in a case study of the Wood–Berry distillation column system.
0925-2312
588-597
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Chen, Gongquan
0ae2584b-f2f0-4fc7-8413-5cb8c5df886f
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Tian, Xuemin
5b7f2306-69c1-41c7-8cab-49932ac1ae01
Chen, Gongquan
0ae2584b-f2f0-4fc7-8413-5cb8c5df886f
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80

Tian, Xuemin, Chen, Gongquan and Chen, Sheng (2011) A data-based approach for multivariate model predictive control performance monitoring Neurocomputing, 74, (4), pp. 588-597.

Record type: Article

Abstract

An intelligent statistical approach is proposed for monitoring the performance of multivariate model predictive control (MPC) controller, which systematically integrates both the assessment and diagnosis procedures. Model predictive error is included into the monitored variable set and a 2-norm based covariance benchmark is presented. By comparing the data of a monitored operational period with the "golden" user-predefined one, this method can properly evaluate the performance of an MPC controller at the monitored operational stage. Characteristic direction information is mined from the operating data and the corresponding classes are built. The eigenvector angle is defined to describe the similarity between the current data set and the established classes, and an angle-based classifier is introduced to identify the root cause of MPC performance degradation when a poor performance is detected. The effectiveness of the proposed methodology is demonstrated in a case study of the Wood–Berry distillation column system.

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More information

Published date: January 2011
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 271822
URI: https://eprints.soton.ac.uk/id/eprint/271822
ISSN: 0925-2312
PURE UUID: c2c3141b-97a9-4782-a56f-435d3b2e2bf0

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Date deposited: 21 Dec 2010 09:40
Last modified: 18 Jul 2017 06:38

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Contributors

Author: Xuemin Tian
Author: Gongquan Chen
Author: Sheng Chen

University divisions

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