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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Lennox, B. Goulding, P. |
| Copyright Year | 2005 |
| Description | Author affiliation: Sch. of Electr. & Electron. Eng., Manchester Univ., UK (Lennox, B.; Goulding, P.) |
| Abstract | This paper describes two methods by which multivariate statistics can be exploited to provide benefits to industrial process control. In the first application, the inner structure of a partial least squares model is applied to provide automatic model switching in piecewise linear systems. In the second application, multivariate statistical noise models, together with kernel density techniques, are used to determine the uncertainty in critical plant sensors. Based upon this uncertainty, it is shown that the set-points and constraints used within a model predictive control algorithm can be manipulated to ensure quality constraints are not violated. |
| Starting Page | 640 |
| Ending Page | 645 |
| File Size | 3591188 |
| Page Count | 6 |
| File Format | |
| ISBN | 0780391373 |
| DOI | 10.1109/ICCA.2005.1528195 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2005-06-26 |
| Publisher Place | Hungary |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Statistics Process control Uncertainty Electrical equipment industry Industrial control Least squares methods Piecewise linear techniques Kernel Predictive models Predictive control |
| Content Type | Text |
| Resource Type | Article |
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