A pragmatic approach for selecting weight matrix coefficients in model predictive control algorithm and its application

Proceedings of the 2009 IEEE International Conference on Automation and Logistics, ICAL 2009

Volumn , Issue , 2009, Pages 486-492

  An, Aimin ^a   Hao, Xiaohong ^a   Zhao, Chao ^b   Su, Hongye ^b  

^a LANZHOU UNIVERSITY OF TECHNOLOGY   (China)

^b ZHEJIANG UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CONTROLLED SYSTEM; ENERGY ANALYSIS; GRAMIANS; MODEL PREDICTIVE CONTROL ALGORITHM; OBJECTIVE FUNCTIONS; OBSERVABILITY GRAMIANS; SIMULATION RESULT; SINGULAR VALUES; WEIGHT COEFFICIENTS; WEIGHT MATRICES; WEIGHT VALUES;

ALGORITHMS; COMPUTATIONAL EFFICIENCY; CONTROLLABILITY; ENERGY MANAGEMENT; INDUSTRIAL APPLICATIONS; OBSERVABILITY; PREDICTIVE CONTROL SYSTEMS; SIMULATORS;

MODEL PREDICTIVE CONTROL;

EID: 70450201821     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICAL.2009.5262874     Document Type: Conference Paper

References (15)

1. J. Qin, T. Badgwell. A survey of industrial model predictive control technology. Contr. Eng. Prac, 2003, 11: 773-764.
2. N. Agarwal, B. Huang. Assessment model predictive control (MPC) perfoamance 2. Bayesian approach for constraint Tuning. Ind. Eng. Chem. Res., 2007, 46: 7885-7896.
3. M. Morali, J. H. Lee. Model predictive congtrol: past, present and future. Comput. Chem. Eng., 1999, 23: 667-682.
4. R. Bars, R. Habert. Predictive Control applied for linear and nonlinear plants. Techniacal Report, Budapesr University of Technology and Economics, Budapest, Hungary, 2006.
5. Y. Zhou, J. F. Forbes. Determining control benefits via probobilistic optimization. Int. J. Adaptive Control Signal Process, 2003, 17: 553-568
6. D. W. Clarke. Application of generalized predictive control to industrial process IEEE Control Syst. Mag., 1998, April: 49-54
7. B. C. Moore. Principle component analysis in linear systems: controllability, observability, and model reduction. IEEE T Automat. Contr., 1981, 26(1): 17-32.
8. th IEEE Conference on Control Application, Albany, 1995, 663-664.
9. F. Xu, B. Huang, and S. Akande. Performance assessment of model predictive control for variability and constraint tuning. Ind. Eng. Chem. Res., 2007, 46: 1208-1219.
10. G. J. Luo, G. Wu. Study on relationship between smoothing factor and weight in predictive control. J. of Uni. of Sci. and Tech. of China, 2006, 46(8): 856-860.
11. Y. L. Huang, H. H. Lou, J. P. Gong, T. Edgar Fuzzy model predictive control. IEEE Trans. Fuzzy Syst, 2003.
12. H. Chen, F Allgower. A quasi-infinite horizon nonlinear predictive control schema for stable systems Application to a CSTR. In Pro IFAC Advanced Control of Chemical Process, 1997, Banff, Canada: 529-534.
13. st IFAC YAC'95, 1995, Beijing, China: 529-534.
14. Y Ying, M. Rao, Y Sun. Bilinear control strategy for paper making process. Chemical Engineering Communication, 1992, 111: 13-28.
15. D. Dougherty, D. J. Cooper. Tuning guidelines of dynamic matrix controller for intergrating(non-self regulating) process. Ind. Eng. Chem. Res., 2003, 42(8): 1739-1752.