This thesis is concerned with subspace identification and its applications for controller performance assessment and process modeling from closed-loop data.
A joint input-output closed-loop subspace identification method is developed which provides consistent estimation of the subspace matrices and the noise covariance matrix required for the LQG benchmark curve estimation.
Subspace LQG benchmark is also used for performance assessment of the cascade supervisory-regulatory control systems.
Three possible scenarios for LQG control design and performance improvement are discussed for this structure. A closed-loop subspace identification method is also provided for estimation of the subspace matrices necessary for performance assessment.
A method of direct step model estimation from closed-loop data is provided using subspace identification. The variance calculation required for this purpose can be performed using the proposed method. The variances are used for weighted averaging on the estimated Markov parameters to attenuate the noise influence on the final step response estimation. / Process Control
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:AEU.10048/519 |
| Date | 11 1900 |
| Creators | Danesh Pour, Nima |
| Contributors | Huang, Biao (Chemical and Materials Engineering), Shah, Sirish L. (Chemical and Materials Engineering), Chen, Tongwen (Electrical and Computer Engineering), Lee, Jong Min (Chemical and Materials Engineering) |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | English |
| Detected Language | English |
| Type | Thesis |
| Format | 2235142 bytes, application/pdf |
Page generated in 0.0021 seconds