Achieving state estimation equivalence for misassigned disturbances in offset-free model predictive control

AIChE Journal

Volumn 55, Issue 2, 2009, Pages 396-407

  Rajamani, Murali R ^a   Rawlings, James B ^b   Qin, S Joe ^c  

^a BP Research and Technology ^*  (United States)

^b Wisconsin Electric Machines and Power Electronics Consortium   (United States)

^c University of Southern California   (United States)

Author keywords

Disturbance models; Model equivalence; Model predictive control; State estimation

Indexed keywords

ADVANCED CONTROL STRATEGIES; AUTOCOVARIANCE; BEST CHOICES; CLOSED-LOOP PERFORMANCE; DISTURBANCE MODELS; MODEL EQUIVALENCE; NOISE DISTURBANCES; PLANT INPUTS; PLANT OUTPUTS; STATE ESTIMATORS; UNKNOWN DISTURBANCES; UNMODELED DISTURBANCES;

ESTIMATION; PREDICTIVE CONTROL SYSTEMS; STATE ESTIMATION;

MODEL PREDICTIVE CONTROL;

EID: 63449110916     PISSN: 00011541     EISSN: 15475905     Source Type: Journal    
DOI: 10.1002/aic.11673     Document Type: Article

References (43)

1. Qin SJ, Badgwell TA. A survey of industrial model predictive control technology. Control Eng Practice. 2003;11:733-764.
2. Rawlings JB, Muske KR. Stability of constrained receding horizon control. IEEE Trans Autom Control. 1993;38:1512-1516.
3. Scokaert POM, Rawlings JB, Meadows ES. Discrete-time stability with perturbations: application to model predictive control. Automatica. 1997;33:463-470.
4. Muske KR, Rawlings JB. Model predictive control with linear models. AIChE J. 1993;39:262-287.
5. Davison EJ, Smith HW. Pole assignment in linear time-invariant multivariable systems with constant disturbances. Automatica. 1971; 7:489-498.
6. Morari M, Stephanopoulos G III. Optimal selection of secondary measurements within the framework of state estimation in the presence of persistent unknown disturbances. AIChE J. 1980;26:247-260.
7. Richalet J, Rault A. Testud JL, Papon J. Model predictive heuristic control: applications to industrial processes. Automatica. 1978;14:413-428.
8. Cutler CR, Ramaker BL. Dynamic matrix control - a computer control algorithm. In: Proceedings of the Joint Automatic Control Conference. 1980.
9. Marquis P, Broustail JP. SMOC, a bridge between state space and model predictive controllers: application to the automation of a hydrotreating unit. In: McAvoy TJ, Arkun Y, Zafiriou E, editors. Proceedings of the 1988 IFAC Workshop on Model Based Process Control. Oxford: Pergamon Press, 1988:37-43.
10. Pannocchia G, Rawlings JB. Disturbance models for offset-free MPC control. AIChE J. 2003;49:426-437.
11. Muske KR, Badgwell TA. Disturbance modeling for offset-free linear model predictive control. J Process Control. 2002;12:617-632.
12. Downs JJ. Control strategy design using model predictive control. In: Proceedings of the American Control Conference. San Diego, CA, 1999.
13. Pannocchia G, Bemporad A. Combined design of disturbance model and observer for offset-free model predictive control. IEEE Trans Autom Control. 2007;52:1048-1053.
14. Ho BL, Kalman RE. Efficient construction of linear state variable models from input/output functions. Regelungstechnik. 1966;14:545-548.
15. Odelson BJ, Rajamani MR, Rawlings JB. A new autocovariance least-squares method for estimating noise covariances. Automatica. 2006;42:303-308.
16. Overschee PV, Moor BD. Subspace Identification for Linear Systems: Theory, Implementation, Applications. Boston, MA: Kluwer Academic Publishers, 1996.
17. Gevers M. A personal view of the development of system identification. IEEE Control Syst Mag. 2006;26:93-105.
18. Ljung L. System Identification - Theory for the User, 2nd ed. New Jersey: Prentice Hall, 2000.
19. Francis BA, Wonham WM. The internal model principle for linear multivariable regulators. J Appl Math Optim. 1975;2:170-195.
20. Francis BA, Wonham WM. The internal model principle of control theory. Automatica. 1976;12:457-465.
21. Rao CV, Rawlings JB, Lee JH. Constrained linear state estimation - a moving horizon approach. Automatica. 2001;37:1619-1628.
22. Lee JH. Morari M. García CE, State-space interpretation of model predictive control. Automatica. 1994;30:707-717.
23. Pannocchia G. Robust disturbance modeling for model predictive control with application to multivariable ill-conditioned processes. J Process Control. 2003;13:693-701.
24. Eppler S. Steady offset and integral control in model predictive control. In: Advanced Independent Study. Madison: University of Wisconsin, 1997.
25. Shinskey FG. Feedback Controllers for the Process Industries. New York: McGraw-Hill, 1994.
26. Anderson BDO, Moore JB. Optimal Control: Linear Quadratic Methods. New Jersey: Prentice-Hall, 1990. ISBN 0-13-638560-5.
27. Muske KR. Rawlings JB. Linear model predictive control of unstable processes. J Process Control. 1993;3:85-96.
28. Pannocchia G. Robust offset-free model predictive control. Presented at the IFAC 2002 World Congress, Barcelona, Spain, 2002.
29. García CE, Morshedi AM. Quadratic programming solution of dynamic matrix control (QDMC). Chem Eng Commun. 1986;46:73-87.
30. Box GEP, Jenkins GM. Time Series Analysis: Forecasting and Control, 1st ed. San Francisco, CA: Holden-Day, 1970.
31. Box GEP, Jenkins GM. Reinsel GC. Time Series Analysis: Forecasting and Control, 3rd ed. Englewood Cliffs, NJ: Prentice Hall, 1994.
32. Morari M, Lee JH. Model predictive control: the good, the bad, and the ugly. In: Arkun Y, Ray WH, editors. Chemical Process Control-CPC IV. Fourth International Conference on Chemical Process Control. Amsterdam: Elsevier, 1991;419-444.
33. Li S, Lim KY, Fisher DG. A state space formulation of model predictive control. AIChE J. 1989;35:241-249.
34. Lundström P. Lee JH, Morari M, Skogestad S. Limitations of dynamic matrix control. Comput Chem Eng. 1995;19:409-421.
35. Gupta YP. Control of integrating processes using dynamic matrix control. Chem Eng Res Des. 1998;76:465-470.
36. Dougherty D, Cooper DJ. Tuning guidelines of a dynamic matrix controller for integrating (non-self-regulating) processes. Ind Eng Chem Res. 2003;42:1739-1752.
37. Jenkins GM, Watts DG. Spectral Analysis and its Applications. San Francisco, CA: Holden-Day, 1968.
38. Rajamani MR, Rawlings JB.Estimation of the disturbance structure from data using semidefinite programming and optimal weighting. Automatica. 2009;45:142-148.
39. Searle SR. Matrix Algebra Useful for Statistics. New York: Wiley, 1982.
40. Graham A. Kronecker Products and Matrix Calculus With Applications. West Sussex, England: Ellis Horwood Ltd., 1981.
41. Rajamani MR. Data-Based Techniques to Improve State Estimation in Model Predictive Control, Ph.D. Thesis. Madison: University of Wisconsin, 2007.
42. Åkesson BM, Jørgensen JB, Poulsen NK, SB Jørgensen. A generalized autocovariance least-squares method for Kalman filter tuning. J Process Control. 2008;18:769-779.
43. Rajamani MR, Rawlings JB, Qin SJ, Downs JJ. Equivalence of MPC disturbance models identified from data. In; Proceedings of Chemical Process Control-CPC 7, Alberta, Canada, 2006.