Identification for decentralized model predictive control

AIChE Journal

Volumn 52, Issue 6, 2006, Pages 2198-2210

  Gudi, Ravindra D ^a   Rawlings, James B ^b  

^a INDIAN INSTITUTE OF TECHNOLOGY BOMBAY   (India)

^b University of Wisconsin Madison   (United States)

Author keywords

Closed loop identification; Decentralized identification

Indexed keywords

CLOSED-LOOP IDENTIFICATION; CONSTRAINED CONTROLLERS; DECENTRALIZED CONTROLS; DECENTRALIZED IDENTIFICATION;

CLOSED LOOP CONTROL SYSTEMS; CONSTRAINT THEORY; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS;

PREDICTIVE CONTROL SYSTEMS;

CLOSED LOOP CONTROL SYSTEMS; CONSTRAINT THEORY; KNOWLEDGE BASED SYSTEMS; MATHEMATICAL MODELS; PREDICTIVE CONTROL SYSTEMS;

EID: 33744804019     PISSN: 00011541     EISSN: 15475905     Source Type: Journal    
DOI: 10.1002/aic.10781     Document Type: Article

References (21)

1. Siljak DD. Decentralized control and computations: status and prospects. Annual Reviews in Control. 1996;20:131-142.
2. Mesarovic MD, Macko D, Takahara. Two coordination principles and their application in large scale systems control. Automatica. 1970;6: 2647-2473.
3. Katebi MR, Johnson MA. Predictive control design for large scale systems. Automatica. 1997;33(3):421-425.
4. Venkat A, Rawlings JB, Wright S. Plant-wide optimal control with decentralized MPC. In: Proceedings of International Symposium on Dynamics and Control of Process Systems (DYCOPS2004). Cambridge, MA: Paper No. 190. Elsevier Press; 2004.
5. Forssell U. Ljung L. Closed-loop identification revisited. Automatica. 1999;35(7):1215-1241.
6. Gevers M, Ljung L. Optimal experiment designs with respect to the intended model application. Automatica. 1986;22(4):543-555.
7. Zang Z, Bitmead RR, Gevers M. Iterative weighted least squares identification and weighted LQG control design. Automatica. 1995; 31(11):1577-1594.
8. Ljung L. System Identification: Theory for the User. NJ: Prentice Hall; 1997.
9. Forssell U, Ljung L. A projection method for closed loop identification. IEEE Trans on AC. 2000;45(11):2101-2105.
10. Goodwin GC, Graebe SF, Salgado ME. Control System Design. NJ: Prentice Hall; 2000.
11. Verhaegen M, Yu X. A class of subspace model identification algorithms to identify periodically and arbitrarily time-varying systems. Automatica. 1995;31(2):201-216.
12. Zafiriou E. Robust model predictive control of processes with hard constraints. Computers and Chemical Engineering. 1990;14(4/5):359-371.
13. Draper NR, Smith H. Applied Regression Analysis. New York: John-Wiley; 1993.
14. Smillie KW. Introduction to Regression and Correlation. Toronto: Academic Press; 1966.
15. Shah SL, Huang B. Patwardhan RS. Multivariate controller performance analysis: methods, applications and challenges. In: CPC-VI, Proceedings of Sixth International Conference on Chemical Process Control. Rawlings JB, Ogunnaike BA, Eaton JW, eds. AIChE Symposium Series. 2002;98:187-219.
16. Van den Hof PMJ, Schrama RJP. An indirect method for transfer function estimation using closed loop data. Automation. 1993;29(6): 1523-1527.
17. Huang B, Shah SL. Closed-loop identification: a two step approach. Journal of Process Control. 1997;7(6):425-438.
18. Gevers M, Ljung L, Van den Hof P. Asymptotic variance expressions for closed loop identification. Automatica. 2001;37:781-786.
19. Congalidis JP, Richards JR, Ray WH. Modeling and control of co-polymerization reactor. Proceedings of American Control Conference. 1986;1779-1793.
20. Godfrey K. Perturbation Signals for System Identification. New York: Prentice Hall; 1993.
21. Srinivasan R, Raghunathan R. Use of inverse repeat sequence for identification in chemical process systems. Industrial and Engineering Chemistry Research. 1999;38(9):3420-3429.