Abstract
A novel integrated learning control system is presented in this paper. It systematically integrates discrete-time (batch-axis) information and continuous-time (time-axis) information into one uniform frame. More specifically, the iterative learning controller is designed in the domain of batch-axis, while an adaptive single neuron predictive controller (SNPC) in the domain of time-axis. In addition, the convergence and tracking performance of the proposed integrated learning control system are firstly given rigorous description and proof. Lastly, to verify the effectiveness of the proposed integrated control system, it is applied to a benchmark batch process, in comparison with ILC recently developed.
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References
Xiong, Z.H., Zhang, J., Wang, X., Xu, Y.M.: Integrated tracking control strategy for batch processes using a batch-wise linear time-varying perturbation model. Control Theory & Applications 1(1), 178–188 (2007)
Lu, N., Gao, F.: Stage-based process analysis and quality prediction for batch processes. Industrial and Engineering Chemistry Research 44(10), 3547–3555 (2005)
Amann, N., Owens, D.H., Rogers, E.: Iterative learning control for discrete-time system with exponential rate of convergence. IEE Proc., Control Theory Appl. 143, 217–224 (1996)
Liu, T., Gao, F.-R.: IMC-based iterative learning control for batch processes with uncertain time delay. Journal of Process Control 20, 173–180 (2010)
Lee, K.S., Lee, J.H.: Convergence of constrained model-based predictive control for batch processes. IEEE Trans. Autom. Control 45, 1928–1932 (2000)
Lee, K.S., Chin, I.S., Lee, H.J., Lee, J.H.: Model predictive control technique combined with iterative learning control for batch processes. AIChE J. 45, 2175–2187 (1999)
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, 641–657 (2000)
Lee, K.S., Chin, I.-S., Lee, J.H.: Model Predictive Control Technique Combined with Iterative Learning for Batch Processes. AIChE Journal 45(10), 2175–2187 (1999)
Xiong, Z.H., Zhang, J.: Tracking control for batch processes through integrating batch-to-batch interative learning control and within-batch on-line control
Xiong, Z.H., Zhang, J.: Product quality trajectory tracking of batch processes using iterative learning control based on linear perturbation model. Ind. Eng. Chem. Res. 42, 6802–6814 (2003); American Chemrican Society 44, 3988–3992 (2005)
Xiong, Z.H., Zhang, J.: Batch-to-batch optimal control of nonlinear batch processes based on incrementally updated models. IEE Proc., Control Theory Appl. 151, 158–165 (2004)
Rogers, E., Owens, D.H.: Stability analysis for linear repetitive processes. Springer, Heidelberg (1992)
Kurek, J.E., Zaremba, M.B.: Iterative learning control synthesis based on 2-D system theory. IEEE Transactions on Automatic Control 38(1), 121–125 (1993)
Fang, Y., Chow, T.W.S.: 2-D analysis for iterative learning controller for discrete-time systems with variable initial conditions. IEEE Transactions on Circuits and Systems-I: Fundamental Theory and Applications 50(5), 722–727 (2003)
Shi, J., Gao, F., Wu, T.: 2D Model Predictive Iterative Learning Control Schemes for Batch Processes. In: IFAC International Symposium on Advanced Control of Chemical Processes 2006, Gramado, Brazil, pp. 215–220 (2006)
Shi, J., Gao, F., Wu, T.: From Two-dimensional Linear Quadratic Optimal Control to Iterative Learning Control Paper 1. Two-dimensional Linear Quadratic Optimal Controls and System Analysis. Industrial & Engineering Chemistry Research 45(13), 4603–4616 (2006)
Shi, J., Gao, F., Wu, T.: From Two-dimensional Linear Quadratic Optimal Control to Iterative Learning Control Paper 2. Iterative Learning Controls for Batch Processes. Industrial & Engineering Chemistry Research 45(13), 4617–4628 (2006)
Jia, L., Shi, J.-P., Chiu, M.-S.: Fuzzy neural model with global convergence for batch process. Information and Control 38(6), 1–7 (2009)
Jia, L., Cao, L.-M., Chiu, M.-S.: Data Driven-Adaptive Single Neuron Predictive Controller Based on Lyapunov Approach. In: WCICA 2011 - 2011 World Congress on Intelligent Control and Automation, Conference Digest, pp. 7–12 (2011)
Jia, L., Cao, L., Chiu, M.-S.: Data-driven Based Integrated Learning Control for Batch Processes. In: IFAC 2012 International Symposium on Advanced Control of Chemical Processes, Singapore, pp. 234–238 (2012)
Jia, L., Shi, J.-P.: R-adjustable learning control for batch process. Control Theory & Applications 28(9), 1159–1162 (2011)
Lu, N., Gao, F.: Stage-based process analysis and quality prediction for batch processes. Industrial and Engineering Chemistry Research 44(10), 3547–3555 (2005)
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Jia, L., Cao, L., Chiu, M. (2013). Analysis on Data-Based Integrated Learning Control for Batch Processes. In: Li, K., Li, S., Li, D., Niu, Q. (eds) Intelligent Computing for Sustainable Energy and Environment. ICSEE 2012. Communications in Computer and Information Science, vol 355. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37105-9_15
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DOI: https://doi.org/10.1007/978-3-642-37105-9_15
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