Abstract:
In multivariate model predictive control (MPC) systems, the quality of multi-input multi-output (MIMO) plant models has significant impact on the controller performance i...Show MoreMetadata
Abstract:
In multivariate model predictive control (MPC) systems, the quality of multi-input multi-output (MIMO) plant models has significant impact on the controller performance in different aspects. Though re-identification of plant models can improve model quality and prediction accuracy, it is very time consuming and economically expensive in industrial practice. Therefore, the automatic detection and isolation of the model-plant mismatch is highly desirable to monitor and improve MPC performance. In this paper, a new closed-loop MPC performance monitoring approach is proposed to detect model-plant mismatch within state-space formulations through subspace projections and statistical hypothesis testing. A monitoring framework consisting of three quadratic indices is developed to capture model-plant mismatches precisely. The validity and effectiveness of the proposed method is demonstrated through a paper machine headbox control example.
Published in: 52nd IEEE Conference on Decision and Control
Date of Conference: 10-13 December 2013
Date Added to IEEE Xplore: 10 March 2014
ISBN Information:
Print ISSN: 0191-2216
References is not available for this document.
Contents Select All
1.
M. Morari, J.H. Lee, Model predictive control: past present and future, Camp. Chem. Eng., vol. 23, 1999, pp. 667-682.
2.
M. Kano and M. Ogawa, The state of the art in advanced chemical process control in Japan, in IFAC Symposium on Advanced Control of Chemical Processes (ADCHEM), Istanbul, Turkey, 2009.
3.
TJ. Harris, Assessment of control loop performance, Can. J. Chem. Eng., vol. 67, 1989, pp. 856-861.
4.
J. Yu and S.l. Qin, Statistical MIMO controller performance monitoring. Part I: Data-driven covariance benchmark, J. Proc. Cant., vol. 18, 2008, pp. 277-296.
5.
J. Yu and S.l. Qin, Statistical MIMO controller performance monitoring. Part II: Performance diagnosis, J. Proc. Cant., vol. 18, 2008, pp. 297-319.
6.
R.H. Julien, M.W Foley, and WR. Cluett, Performance assessment using a model predictive control benchmark, J. Proc. Cant., vol. 14, 2004, pp. 441-456.
7.
J. Yu and SJ. Qin, MIMO control performance monitoring using lefUright diagonal interactors, J. Proc. Cant., vol. 19, 2009, pp. 1267- 1276.
8.
J. Schafer, and A. Cinar. Multivariable MPC system performance assessment, monitoring, and diagnosis, J. Proc. Cant., vol. 14, 2004, pp. 113-129.
9.
B. Huang, Bayesian methods for control loop monitoring and diagnosis, J. Proc. Cant., vol. 18, 2008, pp. 829-838.
10.
A.S. Badwe, R.D. Gudi, R.S. Patwardhan, S.L. Shah, and S.c. Patwardhan, Detection of model-plant mismatch in MPC applications. Proc. Cant., vol. 19, 2009, pp. 1305-1313.
11.
M. Kano, Y. Shigi, S. Hasebe, and S. Ooyama, Detection of significant model-plant mismatch from routine operation data of model predictive control system, in the 9th International Symposium on Dynamics and Control of Process Systems (DYCOPS), Leuven, Belgium, 2010.
12.
Z. Sun, SJ. Qin, A. Singhal, and L. Megan, Control performance monitoring via model residual assessment, in American Control Conference, Montreal, Canada, June 2012.
13.
H. Jiang, W. Li, and S.L. Shah, Detection and isolation of modelplant mismatch for multivariate dynamic systems, in the 6th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes, Beijing, China, 2006, pp.1396-1401.
14.
SJ. Qin, An overview of subspace identification, Comput. Chem. Eng., vo1.30, 2006, pp. 1502-1513.
15.
B. Huang, S.X. Ding, and SJ. Qin, Closed-loop subspace identification: An orthogonal projection approach,. Proc. Cant., vol. 15, 2005, pp. 53-66.
16.
T.W. Anderson, An introduction to multivariate statistical analysis, 3rd ed., Wiley, New York, NY, USA, 2003.
