A data-based approach for multivariate model predictive control performance monitoring
Tian, Xuemin, Chen, Gongquan and Chen, Sheng (2011) A data-based approach for multivariate model predictive control performance monitoring. Neurocomputing, 74, (4), 588-597.
- Published Version
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.
|Divisions:||Faculty of Physical and Applied Science > Electronics and Computer Science > Comms, Signal Processing & Control
|Date Deposited:||21 Dec 2010 09:40|
|Last Modified:||25 Aug 2012 02:20|
|Contributors:||Tian, Xuemin (Author)
Chen, Gongquan (Author)
Chen, Sheng (Author)
|Further Information:||Google Scholar|
|ISI Citation Count:||1|
|RDF:||RDF+N-Triples, RDF+N3, RDF+XML, Browse.|
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