Abstract
An optimal iterative learning control (ILC) strategy of improving endpoint products in semi-batch processes is presented by combining a neural network model. Control affine feed-forward neural network (CAFNN) is proposed to build a model of semi-batch process. The main advantage of CAFNN is to obtain analytically its gradient of endpoint products with respect to input. Therefore, an optimal ILC law with direct error feedback is obtained explicitly, and the convergence of tracking error can be analyzed theoretically. It has been proved that the tracking errors may converge to small values. The proposed modeling and control strategy is illustrated on a simulated isothermal semi-batch reactor, and the results show that the endpoint products can be improved gradually from batch to batch.
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References
Bonvin D. Optimal operation of batch reactors—A personal view. J Process Contr, 1998, 8: 355–368
Park S, Ramirez W F. Optimal production of secreted protein in fed-batch reactors. AIChE J, 1988, 34: 1550–1558
Bristow D A, Tharayil M, Alleyne A G. A survey of iterative learning control. IEEE Contr Syst Mag, 2006, 26: 96–114
Lee J H, Lee K S. Iterative learning control applied to batch processes: an overview. Contr Eng Pract, 2007, 15: 1306–1318
Owens D H, Hatonen J. Iterative learning control—An optimization paradigm. Ann Rev Contr, 2005, 29: 57–70
Amann N, Owens D H, Rogers E. Iterative learning control for discrete-time system with exponential rate of convergence. IEE Proc D -Contr Theor Appl, 1996, 143: 217–224
Lee J H, Lee K S, Kim W C. Model-based iterative learning control with a quadratic criterion for time-varying linear systems. Automatica, 2000, 36: 641–657
Lee K S, Lee J H. Iterative learning control-based batch process control technique for integrated control of end product properties and transient profiles of process variables. J Process Contr, 2003, 13: 607–621
Xiong Z H, Zhang J. Product quality trajectory tracking of batch processes using iterative learning control based on time varying perturbation model. Ind Eng Chem Res, 2003, 42: 6802–6814
Zhang J. A reliable neural network model based optimal control strategy for a batch polymerisation reactor. Ind Eng Chem Res, 2004, 43: 1030–1038
Hunt K J, Sbarbaro D, Zbikowski R, et al. Neural networks for control systems—A survey. Automatica, 1992, 28: 1083–1112
Xiong Z H, Zhang J. A batch-to-batch iterative optimal control strategy based on recurrent neural network models. J Process Contr, 2005, 15: 11–21
Zhang J. A neural network based strategy for the integrated batch-to-batch control and within batch control of batch processes. Trans Inst Meas Contr, 2005, 27: 391–410
Xiong Z H, Zhang J. Modelling and optimal control of fedbatch processes using a novel control affine feedforward neural network. Neurocomputing, 2004, 61: 317–337
Xiong Z H, Zhang J, Wang X, et al. Neural network based online shrinking horizon re-optimization of fed-batch processes. Lect Notes Comput Sci, 2005, 3498: 839–844
Russell S A, Kesavan P, Lee J H. Recursive data-based prediction and control of batch product quality. AIChE J, 1998, 44: 2442–2458
Terwiesch P, Ravemark D, Schenker B, et al. Semi-batch process optimization under uncertainty: theory and experiments. Comput Chem Eng, 1998, 22: 201–213
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Supported by the National Natural Science Foundation of China (Grant Nos. 60404012, 60874049), the National High-Tech Research & Development Program of China (Grant No. 2007AA041402), the New Star of Science and Technology of Beijing City (Grant No. 2006A62), and the IBM China Research Lab 2008 UR-Program
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Xiong, Z., Dong, J. & Zhang, J. Optimal iterative learning control for end-point product qualities in semi-batch process based on neural network model. Sci. China Ser. F-Inf. Sci. 52, 1136–1144 (2009). https://doi.org/10.1007/s11432-009-0123-8
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DOI: https://doi.org/10.1007/s11432-009-0123-8