Name
Title | ||
|---|---|---|
A data-based approach for multivariate model predictive control performance monitoring |
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1016/j.neucom.2010.09.018 | Neurocomputing |
Keywords | Field | DocType |
monitored variable set,poor performance,mpc controller,mpc performance degradation,multivariate model,model predictive error,performance assessment,monitored operational period,operating data,predictive control performance monitoring,model predictive control,performance diagnosis,data-based approach,performance monitoring,intelligent system,monitored operational stage,eigenvector angle based classifier,current data,eigenvectors,prediction error,procedural modeling | Data mining,Control theory,Model predictive control,Fractionating column,Artificial intelligence,Classifier (linguistics),Root cause,Eigenvalues and eigenvectors,Covariance,Control theory,Multivariate statistics,Machine learning,Mathematics | Journal |
Volume | Issue | ISSN |
74 | 4 | Neurocomputing |
Citations | PageRank | References |
2 | 0.41 | 3 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xuemin Tian | 1 | 71 | 7.54 |
| Gongquan Chen | 2 | 2 | 0.41 |
| Sheng Chen | 3 | 1294 | 92.85 |