Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/63474
Title: An iterative learning control algorithm with convergence analysis for batch processes
Authors: Jia, L.
Shi, J.
Chiu, M. 
Keywords: Batch process
Iterative learning
Neural fuzzy model
Optimization algorithm
Issue Date: Jan-2010
Source: Jia, L.,Shi, J.,Chiu, M. (2010-01). An iterative learning control algorithm with convergence analysis for batch processes. Huagong Xuebao/CIESC Journal 61 (1) : 116-123. ScholarBank@NUS Repository.
Abstract: It is difficult to analyze the convergence of iterative learning optimal control for quality control of batch processes, and there exist the disturbance and uncertainties in practical processes. In this paper, a neural fuzzy (NF) model-based approach was used to predict the quality of product and an adaptive update algorithm in the direction of batch was also presented after analyzing the problem of parameters dynamic updating. On this basis, an iterative learning control algorithm with convergence analysis for batch processes was proposed. Moreover, the convergence of the proposed algorithm was analyzed and the rigorous proof was given. Lastly, to verify the efficiency of the proposed algorithm, the algorithm was applied to a classical batch process, and the simulation results showed the efficiency and practicability of the proposed method. Thus it provides a new way for the control of batch processes. © All Rights Reserved.
Source Title: Huagong Xuebao/CIESC Journal
URI: http://scholarbank.nus.edu.sg/handle/10635/63474
ISSN: 04381157
Appears in Collections:Staff Publications

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