Abstract
In this chapter, a unified framework to combine real-time control with iterative learning is developed for control system design of batch processes. First, a generic model which describes the state transition of a time-varying linear batch process along batch indices as well as time indices is derived in a state space form. Based on this model, constrained and unconstrained predictive control algorithms that utilize past run data along with real-time measurements are devised. It is shown that, by using the information from past batches, perfect tracking can be achieved despite model uncertainty as the number of batch grows. Convergence is established using cost decrease argument under reasonable assumptions.
To highlight the key features of the algorithm, several numerical examples are provided for linear cases. Also to demonstrate the key implementation steps of the algorithm and to investigate its performance in a real process, experiments in a bench-scale batch reactor are presented.
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References
S. Arimoto, S. Kawamura, and F. Miyazaki. Bettering operation of robots by learning. J. of Robotic Systems, 1(2):123–140, 1984.
Yangquan Chen and K. L. Moore. An ILC biliography list (December 1997). http://shuya.ml.org:888/~yqchen/ILC/ilclinks.html.
Y. Chen, J.-X. Xu, T.H. Lee, and S. Yamamoto. An iterative learning control in rapid thermal processing. In Proc. the IASTED Int. Conf. on Modeling, Simulation and Optimization (MSO’97), pages 189–92, Singapore, Aug. 1997.
Y. Chen, J.-X. Xu, T.H. Lee, and S. Yamamoto. Comparative studies of iterative learning control schemes for a batch chemical process. In Proc. of the IEEE Singapore Int. Symposium on Control Theory and Applications (SISCTA’97), pages 166–70, Singapore, Jul. 1997.
Yangquan Chen, Jian-Xin Xu, and Tong Heng Lee. Current iteration tracking error assisted high-order iterative learning control of discrete-time uncertain nonlinear systems. In Proceedings of the 2nd Asian Control Conference, Seoul, Korea, July 1997.
Jeong-Woo Choi, Hyun-Goo Choi, Kwang-Soon Lee, and Won-Hong Lee. Control of ethanol concentration in a fed-batch cultivation of acinetobacter cal-coaceticus RAG-1 using a feedback-assisted iterative learning algorithm. Journal of Biotechnology, 49:29–43, August, 1996.
T.-Y. Kuc, J. S. Lee, and K. Nam. An iterative learning control theory for a class of nonlinear dynamic systems. Automatica, 28(6): 1215–1221, 1992.
J. H. Lee, M. Morari, and C.E. Garcia. State space interpretation of model predictive control., ” Automatica, 30, pp. 707–717, 1994.
J. H. Lee. Recent Advances in model predictive control and other related areas. In Proc. CPC-V, Taho City, USA, Jan. 1996.
K. S. Lee, S. H. Bang, and K. S. Chang. Feedback-assisted iterative learning control based on an inverse process model. J. of Process Control, 4(2):77–89, 1994.
K. S. Lee, S. H. Bang, S. Yi, J. S. Son, and S. C. Yoon. Iterative learning control of heat up phase for a batch polymerization reactor. Journal of Process Control, 6(4):255–262, August 1996.
K. S. Lee, J. H. Lee, and I. S. Chin. A Model-based predictive control technique for combined iterative learning and real-time feedback control of batch processes. IEEE Trans. A.C., submitted in 1997.
D. H. Owens. Iterative learning control — convergence using high gain feedback. In Proc. of the 31st Conf. on Decision and Control, pages 2545–2546, Tucson, Arizona, USA, Dec. 1992.
S. J. Qin and T. A. Badgwell. An overview of industrial model predictive control technology. In Proc. CPC-V, Taho City, USA, Jan. 1996.
E. Zafiriou and H. W. Chiou. Output constraint softening for SISO model predictive control. In Proc. ACC, pages 372–276, San Fransisco, CA, USA, 1993.
E. Zafiriou, R.A. Adomaitis, and G. Gattu. An approach to run-to-run control for rapid thermal processing. In Proc. of American Control Conf., pages 1286–1288, Seattle, WA, USA, 1995.
E. Zafiriou, H. W. Chiou, and R.A. Adomaitis. Nonlinear model based run-to-run control for rapid thermal processing with unmeasured variable estimation. In Electrochemical Society Proceedings (Vol. 95-4), pages 18–31, 1995.
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© 1998 Springer Science+Business Media New York
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Lee, K.S., Lee, J.H. (1998). Model-Based Predictive Control Combined with Iterative Learning for Batch or Repetitive Processes. In: Bien, Z., Xu, JX. (eds) Iterative Learning Control. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-5629-9_16
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DOI: https://doi.org/10.1007/978-1-4615-5629-9_16
Publisher Name: Springer, Boston, MA
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