Hybrid intelligent control strategy of the laminar cooling process

Authors:
Minghao Tan;Shujiang Li;Tianyou Chai
Affiliations:
School of Information Science and Engineering, Shenyang University of Technology, Shenyang, Liaoning, China;School of Information Science and Engineering, Shenyang University of Technology, Shenyang, Liaoning, China;Research Center of Automation, Northeastern University, Shenyang, Liaoning, China
Venue:
ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III
Year:
2006

Citing 4
Cited 0

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Laminar cooling process model development using RBF networks

ISNN'06 Proceedings of the Third international conference on Advances in Neural Networks - Volume Part III

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Abstract

Performance of controlled laminar cooling is usually poor because of the difficulty in continuous online temperature measurement and the complex nature of the laminar cooling process (e.g., highly nonlinear, time varying). This paper developed a hybrid control strategy for the laminar cooling process that integrates Radial Basis Function (RBF) networks and Case-Based Reasoning (CBR). The spraying pattern and the first activated headers are found by a case-based reasoner, while the number of activated headers is calculated in real time by RBF networks. Experimental studies using production data from a hot strip mill show the superior performance of the proposed control strategy.