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Computational Intelligence and Neuroscience
Volume 2016 (2016), Article ID 9731823, 12 pages
http://dx.doi.org/10.1155/2016/9731823
Research Article

Optimization Control of the Color-Coating Production Process for Model Uncertainty

1College of Information Science and Engineering, Northeastern University, Shenyang, Liaoning 110004, China
2State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang 110004, China

Received 26 November 2015; Accepted 27 March 2016

Academic Editor: Chaomin Luo

Copyright © 2016 Dakuo He et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Optimized control of the color-coating production process (CCPP) aims at reducing production costs and improving economic efficiency while meeting quality requirements. However, because optimization control of the CCPP is hampered by model uncertainty, a strategy that considers model uncertainty is proposed. Previous work has introduced a mechanistic model of CCPP based on process analysis to simulate the actual production process and generate process data. The partial least squares method is then applied to develop predictive models of film thickness and economic efficiency. To manage the model uncertainty, the robust optimization approach is introduced to improve the feasibility of the optimized solution. Iterative learning control is then utilized to further refine the model uncertainty. The constrained film thickness is transformed into one of the tracked targets to overcome the drawback that traditional iterative learning control cannot address constraints. The goal setting of economic efficiency is updated continuously according to the film thickness setting until this reaches its desired value. Finally, fuzzy parameter adjustment is adopted to ensure that the economic efficiency and film thickness converge rapidly to their optimized values under the constraint conditions. The effectiveness of the proposed optimization control strategy is validated by simulation results.