Seminar: Model Predictive Control Integrated With Iterative Learning Control for Batch Processes

Model Predictive Control Integrated With Iterative Learning Control for Batch Processes

Jong Min Lee, an Associate Professor in School of Chemical and Biological Engineering and the Director of Engineering Development Research Center (EDRC) at Seoul National University, will present “Model Predictive Control Integrated With Iterative Learning Control for Batch Processes” at 3:00 p.m. on June 21, 2017 in the Frederick E. Giesecke Engineering Research Building (1617 Research Pkwy, College Station, TX 77845) third floor conference room.

Biography

Jong Min Lee is an Associate Professor in School of Chemical and Biological Engineering and the Director of Engineering Development Research Center (EDRC) at Seoul National University (SNU) (Seoul, Korea). From September 2016, he is currently a Visiting Associate Professor in the Department of Chemical Engineering at MIT where he taught Systems Engineering. He also held the Samwha Motors Chaired Professorship from 2015 to 2017.

He obtained B.Sc. degree in Chemical Engineering from SNU in 1996 and completed his Ph.D. in Chemical Engineering at Georgia Institute of Technology (Atlanta, United States) in 2004. He also held a research associate position in Biomedical Engineering at the University of Virginia (Charlottesville, United States) from 2005 to 2006. He was an assistant professor of Chemical and Materials Engineering at University of Alberta (Edmonton, Canada) from 2006 before joining SNU in 2010. He is also a registered professional engineer with APEGA in Alberta, Canada. Dr. Lee has co-authored the book titled “Process Design using HYSYS” (AJIN, 2009). He received teaching excellence award from SNU in 2012, the best paper award in ICCAS 2011 and 2014. His current research interests include modeling, control, and optimization of large-scale chemical process, energy, and biological systems with uncertainty.

Abstract

Iterative learning control (ILC) is an effective control technique for improving tracking performance of batch process under model uncertainty. ILC was originally introduced for robot manipulators [Arimoto et al., 1984], and has been implemented in many industrial processes such as semiconductor manufacturing and chemical batch processes [Xu et al., 1999] [Lee and Lee, 2007] [Ahn et al., 2014]. In many ILC algorithms, input sequences for the current batch are calculated using tracking error sequences of the previous batch. This type of ILC algorithms is open-loop control within a batch and it cannot handle real-time disturbances. ILC should be integrated with real-time feedback control for rejecting real-time disturbances. Model predictive control (MPC) has become the accepted standard for complex constrained multivariable control problems in the process industries. MPC algorithm shows identical performance for all batches and it highly depends on model quality because it does not utilize previous batch information. Although some studies for integrating ILC with MPC have been proposed [Lee et al., 2000] [Chin et al., 2004] [Mo et al., 2012] [Lu et al., 2015], the generalized formulation has not been presented. In this study, we propose a standardized MPC technique combined with an ILC. The proposed technique is called iterative learning model predictive control (ILMPC). The formulation of ILMPC is similar to general model predictive control and relatively simple. Furthermore, all parameters of ILMPC are same as them of MPC and there is no additional parameter. Thus, if MPC is installed in a process, it is easy to apply ILMPC to the process, and it can reflect various considerations such as constraints, disturbance model, time-varying system or stochastic characteristics.

References

1. Ahn, H., Lee, K. S., Kim, M., and Lee., J., Control of a reactive batch distillation process using an iterative learning technique, Korean Journal of Chemical Engineering, 31, 6-11 (2014)
2. Arimoto, S., Kawamura, S., and Miyazaki F., Bettering operation of robots by learning, Journal of Robotic Systems, 1, 123-140 (1984)
3. Chin, I., Qin, S. J., Lee, K. S., and Cho, M., A two-stage iterative learning control technique combined with real-time feedback for independent disturbance rejection, Automatica, 40, 1913-1922 (2004)
4. Lee, J. H. and Lee, K. S., Iterative learning control applied to batch processes: An overview, Control Engineering Practice, 15, 1306-1318 (2007)
5. Lee, J. H., Lee, K. S., and Kim, W. C., Model-based iterative learning control with a quadratic criterion for time-varying linear systems, Automatica, 36, 641-657 (2000)
6. Lu, J. Y., Cao, Z. X., Wang, Z., and Gao, F. R., A two-stage design of two-dimensional model predictive iterative learning control for nonrepetitive disturbance attenuation, Industrial & Engineering Chemistry Research, 54, 5683-5689 (2015)
7. Mo, S. Y., Wang, L. M., Yao, Y., and Gao, F. R., Two-time dimensional dynamic matrix control for batch processes with convergence analysis against the 2D interval uncertainty, Journal of Process Control, 22, 899-914 (2012)
8. Xu, X. J., Chen, Y. Q., Lee, T. H., and Yamamoto, S., Terminal iterative learning control with an application to RTPCVD thickness control, Automatica, 35, 1535-1542 (1999)

Keywords: Batch process; Model predictive control; Iterative learning control.