Abstract
Considering conventional iterative learning control (ILC) is actually an open-loop control approach within each batch, which cannot guarantee the control performance of batch process when uncertainties and disturbances exist, an integrated iterative learning control scheme is presented in this paper. The proposed approach systematically integrates continuous-time information along with time-axis and discrete-time information along with batch-axis into one uniform frame, namely an internal model control (IMC)based PID control along time-axis, while the optimal ILC along batch-axis. As a result, the operation policy of batch process leads to superior tracking performance and better robustness compared with conventional ILC strategy. An illustrative example is exploited to verify the effectiveness of the investigated approach.
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References
Lee, J.H., Lee, K.: Iterative learning control applied to batch processes: An overview. Control Eng. Pract. 15(10), 1306–1318 (2007)
Lee, J.H., Lee, K.S., Kim, W.C.: Model-based iterative learning control with a quadratic criterion for time-varying linear systems. Automatica 36(5), 641–657 (2000)
Xiong, Z., Zhang, J.: A batch-to-batch iterative optimal control strategy based on recurrent neural network models. J. Process Contr. 15, 11–21, 19 (2005)
Rogers, E., Owens, D.H.: Stability analysis for linear repetitive processes. LNCIS, vol. 175. Springer, Heidelberg (1992)
Shi, J., Gao, F., Wu, T.J.: Single-cycle and multi-cycle generalized 2D model predictive iterative learning control schemes for batch processes. J. of Process Contr. 17, 715–727 (2007)
Liu, T., Gao, F., Wang, Y.: IMC-based iterative optimal control for batch processes with uncertain time delay. J. of Process Contr. 20, 173–180 (2010)
Mo, S., Wang, L., Yao, Y., Gao, F.: Two-time dimensional dynamic matrix control for batch processes with convergence analysis against the 2D interval uncertainty. J. of Process Contr. 22, 899–914 (2012)
Jia, L., Yang, T., Qiu, M.: An integrated iterative learning control strategy with model identification and dynamic R-parameter for batch processes. J. of Process Contr. 23, 1332–1341 (2013)
Jia, L., Shi, J., Qiu, M.: Integrated neuro-fuzzy model and dynamic R-parameter based quadratic criterion-iterative learning control for batch process control technique. Neurocomptuing 98, 24–33 (2012)
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Li, Q., Jia, L., Yang, T. (2014). Integrated IMC-ILC Control System Design for Batch Processes. In: Fei, M., Peng, C., Su, Z., Song, Y., Han, Q. (eds) Computational Intelligence, Networked Systems and Their Applications. ICSEE LSMS 2014 2014. Communications in Computer and Information Science, vol 462. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45261-5_41
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DOI: https://doi.org/10.1007/978-3-662-45261-5_41
Publisher Name: Springer, Berlin, Heidelberg
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