Process control applications of subspace and regression-based identification and monitoring methods

Proceedings of the American Control Conference

Volumn 4, Issue , 2005, Pages 2341-2346

  Juricek, Ben C ^a   Seborg, Dale E ^b   Larimore, Wallace E ^c  

^a TOYON RESEARCH CORPORATION   (United States)

^b University of California   (United States)

^c Adaptics Inc   (United States)

Author keywords

Fault detection; Industrial processes; Monitoring; Multivariate regression; Subspace system identification

Indexed keywords

ALGORITHMS; COMPUTER SIMULATION; MATHEMATICAL MODELS; MONITORING; PROCESS CONTROL; REGRESSION ANALYSIS;

FAULT DETECTION; INDUSTRIAL PROCESSES; MULTIVARIATE REGRESSION; SUBSPACE SYSTEM IDENTIFICATION;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 23944432485     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (36)

1. Ljung, L. Issues in System Identification. IEEE Control Systems Magazine, 1991 11(1), 25-29.
2. Box, G.; Jenkins, G.; Reinsel, G. Time Series Analysis: Forecasting and Control; Prentice-Hall: Englewood Cliffs, NJ, 1994.
3. |3] Ljung, L. System Identification; 2nd edition; Prentice-Hall: Upper Saddle River, NJ, 1999.
4. Söderström, T.; Stoica, P. System Identification. Prentice-Hall: Hertfordshire, UK, 1989.
5. Larimore, W. Statistical Optimality and Canonical Variate Analysis System Identification. Signal Processing, 1996 52, 131-144.
6. Wahlberg, B. Model Reductions of High-Order Estimated Models: the Asymptotic ML Approach. Int. J. Control, 1989 49, 169-192.
7. Kailath, T. Linear Systems; Prentice-Hall: Englewood Cliffs, NJ, 1980.
8. Hannan, E.; Deistler, M. The Statistical Theory of Linear Systems; John Wiley: New York, 1988.
9. Qin, S.; Badgwell, T. An Overview of Industrial Model Predictive Control Technology. AIChE Symposium Series 316: CPC-V. Lake Tahoe, CA, 1996 232-256.
10. Mardia, K.; Kent, J.; Bibby, J. Multivariate Analysis; Academic Press, New York, 1979.
11. Burnham, A.; Viveros, R.; MacGregor, J. Frameworks for Latent Variable Multivariate Regression. J. Chemometrics, 1996 10, 31-45.
12. Breiman, L.; Freidman, J. Predicting Multiple Reponses in Multiple Linear Regression. J. Royal Stat. Soc. B. 1997 59, 3-54.
13. Frank, I.; Freidman, J. A Statistical View of Some Chemometrics Regression Tools. Technometrics, 1993 35, 109-148.
14. Tso, M. Reduced-rank Regression and Canonical Analysis. J. Royal Stat. Soc. B, 1981 43, 183 - 189.
15. Stoica, P.; Viberg, M. Maximum Likelihood Parameter and Rank Estimation in Reduced-Rank Multivariate Linear Regressions. IEEE Trans. Signal Processing, 1996 44, 3069-3078.
16. Dayal, B.; MacGregor, J. Multi-output Process Identification. J. Process Control, 1997 7, 269 - 282.
17. Larimore, W. Optimal Reduced Rank Modeling, Prediction, Monitoring, and Control using Canonical Variate Analysis. IFAC 1997 Int. Symp. on Advanced Control of Chemical Processes. Banff, Canada, 1997 61-66.
18. Anderson, T. An Introduction to Multivariate Statistical Analysis; John Wiley: New York, NY, 1984.
19. Larimore, W. Canonical Variate Analysis in Control and Signal Processing. Katayama, T.; Sugimoto, S., Ed., Statistical Methods in Control and Signal Processing. Marcel Dekker, New York, 1997.
20. Larimore, W. Automated Multivariable System Identification and Industrial Applications Proc. American Control Conference, San Diego, CA, 1999 1148-1162.
21. Larimore, W. Automated and Optimal System Identification by Canonical Variables. Proc. American Control Conference. Baltimore, MD, 1994 1640-1644.
22. Larimore, W. Large Sample Efficiency for ADAPTx Subspace System Identification with Unknown Feedback Proc. IFAC Conference on Dynamics and Control of Process Systems, Boston, MA. 2004.
23. Akaike, H. Markovian Representation of Stochastic Processes by Canonical Variables. SIAM J. Control, 1975 13, 162-173.
24. Akaike, H. Canonical correlation and information. Mehra, R.; Lainiotis, D., Ed., System Identification: Advances and Case Studies. Academic Press, 1976.
25. van Overschee, P.; De Moor, B. Subspace Identification for Linear Systems; Kluwer Academic Publishers: Boston, MA, 1996.
26. van Overschee, P.; De Moor, B. N4SID: Subspace Algorithms for the Identification of Combined Deterministic-Stochastic Systems. Automatica, 1994 30, 75-93.
27. van Overschee, P.; De Moor, B. A Unifying Theorem for Three Subspace System Identification Algorithms. Automatica, 1995 31, 1853-1864.
28. Cho, Y.M.; Xu, G.; Kailath, T. Fast Identification of State-Space Models via Exploitation of Displacement Structure. IEEE Trans. Automatic Control, 1994 39, 2004-2017.
29. Basseville, M.; Nikiforov, I. Detection of Abrupt Changes; Prentice-Hall: Englewood Cliffs, NJ, 1995.
30. Juricek, B.; Seborg, D.; Larimore, W. Identification of the Tennessee Eastman Challenge Process Using Subspace Methods. Control Engineering Practice, 2001 9, 1337-1351.
31. Juricek, B.; Seborg, D.; Larimore, W. Reduced Rank ARX and Subspace System Identification for Process Control, Ind. & Eng. Chem. Research, 2002 41, 2185-2203.
32. Juricek, B.; Seborg, D.; Larimore, W. Fault Detection Using Canonical Variate Analysis Ind. & Eng. Chem. Res., 2004 43, 458-474.
33. Seborg, D.; Edgar, T.; Mellichamp, D. Process Dynamics and Control, 2nd ed.; John Wiley: New York, 2004.
34. Downs, J.J., Vogel, E. A plant-wide industrial process control problem, Comp. & Chem. Eng., 1993 17, 245-255.
35. Ricker, N.L. Tennessee Eastman challenge archive, bf 2001 Available at: http://depts.washington.edu/control/LARRY/TE/download.html.
36. X Automated System Identification Software Users Manual. Adaptics, Inc., McLean, VA, 1996.