Improving performance and stability of MPC relevant identification methods

Control Engineering Practice

Volumn 22, Issue 1, 2014, Pages 20-33

  Potts, Alain Segundo ^a   Romano, Rodrigo Alvite ^b   Garcia, Claudio ^a  

^a UNIVERSITY OF SÃO PAULO   (Brazil)

^b INSTITUTO MAUÁ DE TECNOLOGIA   (Brazil)

Author keywords

Identification for control; Model predictive control; MPC relevant identification; Multivariable identification; Process identification

Indexed keywords

CONVERGENCE AND STABILITY; IDENTIFICATION FOR CONTROL; IDENTIFICATION METHOD; IMPROVING PERFORMANCE; MULTI-STEP PREDICTION; MULTIVARIABLE IDENTIFICATIONS; OPEN LOOP SYSTEMS; PROCESS IDENTIFICATION;

ERROR ANALYSIS; MODEL PREDICTIVE CONTROL;

PREDICTIVE CONTROL SYSTEMS;

EID: 84889575020     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2013.09.007     Document Type: Article

References (34)

1. Bayard D.S. High-order multivariable transfer function curve fitting. Algorithms, sparse matrix methods and experimental results. Automatica 1994, 30(9):1439-1444.
2. Cott B. Introduction to the process identification workshop at the 1992 Canadian chemical engineering conference. Journal of Process Control 1995, 5(2):67-69.
3. Cott B. Summary of the process identification workshop at the 1992 Canadian chemical engineering conference. Journal of Process Control 1995, 5(2):109-113.
4. Darby M.L., Nikolaou M. MPC. Current practice and challenges. Control Engineering Practice 2012, 20(4):328-342.
5. Gopaluni, R., Patwardhan, R., & Shah, S. (2002). Bias distribution in MPC relevant identification. In 15th triennial world congress (pp. 639-645).
6. Gopaluni R., Patwardhan R., Shah S. The nature of data pre-filters in MPC relevant identification. Open and closed-loop issues. Automatica 2003, 39(9):1716-1726.
7. Gopaluni R., Patwardhan R., Shah S. MPC relevant identification - tuning the noise model. Journal of Process Control 2004, 14:699-714.
8. Huang B., Malhotra A., Tamayo E. Model predictive control relevant identification and validation. Chemical Engineering Science 2003, 58:2389-2401.
9. Huang B., Shah S. Closed-loop identification. A two step approach. Journal of Process Control 1997, 7(6):425-438.
10. Huang, B. & Wang, Z. (1999). The role of data prefiltering for integrated identification and model predictive control. In 14th world congress of IFAC (pp. 151-156).
11. Jørgensen S.B., Lee J.H. Recent advances and challenges in process identification. AIChE Symposium Series 2002, 98(326):55-74.
12. Jezek J., Kucera V. Efficient algorithm for matrix spectral factorization. Automatica 1985, 21(6):663-669.
13. Kuo B. Digital control systems 1995, Oxford University Press, USA. 2nd ed.
14. Laurí D., Martínez M., Salcedo J., Sanchis J. PLS-based model predictive control relevant identification. PLS-PH algorithm. Chemometrics and Intelligent Laboratory Systems 2010, 100:118-126.
15. Ljung L. Asymptotic variance expressions for identified black-box transfer function models. IEEE Transactions on Automatic Control 1985, 30(9):834-845.
16. Ljung L. System identification. Theory for the user 1999, Prentice Hall. 2nd ed.
17. Ljung L. The system identification toolbox. The manual 2007, The MathWorks Inc., Natick, MA, USA. 7th ed.
18. Ninness B., Hjalmarsson H. Variance error quantifications that are exact for finite-model order. IEEE Transactions On Automatic Control 2004, 49(8):1275-1291.
19. Nocedal J.N., Wright S.J. Numerical optimization 1999, Springer.
20. Ogata K. Discrete-time control systems 1995, Prentice Hall. 2nd ed.
21. Pan Y., Lee J.H. Modified subspace identification for long-range prediction model for inferential control. Control Engineering Practice 2008, 16(12):1487-1500.
22. Pitta, R. N. (2012). Industrial application of model re-identification in closed-loop for MPC. Master's Thesis, Escola Politécnica da Universidade de São Paulo (in Portuguese).
23. Potts, A. S., Romano, R. A. & Garcia, C. (2012). Enhancement in performance and stability of MRI methods. In 16th IFAC symposium on system identification.
24. Rivera D., Jun K. An integrated identification and control design methodology for multivariable process system applications. IEEE Control Systems Magazine 2000, 20(3):25-37.
25. inf and μ-controller synthesis. International Journal of Control 1987, 46(2):505-527.
26. Rossiter J., Kouvaritakis B. Modelling and implicit modelling for predictive control. International Journal of Control 2001, 74(11):1085-1095.
27. Shook D., Mohtadi C., Shah S. A control-relevant identification strategy for GPC. IEEE Transactions on Automatic Control 1992, 37(7):975-980.
28. Shook D.S., Mohtadi C., Shah S.L. Identification for long-range predictive control. IEE Proceedings D 1991, 138(1):75-84.
29. Sotomayar O., Odloak D., Moro L. Closed-loop model re-identification of processes under MPC with zone control. Control Engineering Practice 2009, 17(5):551-563.
30. Ugray Z., Lasdon L., Plummer J., Glover F., Kelly J., Martí R. Scatter search and local NLP solvers. A multistart framework for global optimization. INFORMS Journal on Computing 2007, 19(3):328-340.
31. Wahlberg B. Model reductions of high-order estimated models. The asymptotic ML approach. International Journal of Control 1989, 49(1):169-192.
32. Zhu Y. Multivariable process identification for MPC. The asymptotic method and its applications. Journal of Process Control 1998, 8(2):101-115.
33. Zhu Y. Multivariable system identification for process control 2001, Elsevier Science, Oxford.
34. Zhu Y., Butoyi F. Case studies on closed-loop identification for MPC. Control Engineering Practice 2002, 10(4):403-417.