Modelling and control of coiling entry temperature using interval type-2 fuzzy logic systems

Abstract The set-up of the cooling water applied to the strip as it traverses the runout table in order to achieve the coiler entry temperature was made by an intelligent model implemented using interval type-2 fuzzy logic systems. The model uses as inputs the targets for coiling entry temperature, strip thickness, finish mill exit temperature and finishing mill exit speed. The experiments of this application were carried out for three different types of coil in a real hot strip mill. The results proved the feasibility of the system developed for coiler entry temperature prediction. Comparison with the online type-1 fuzzy logic based model shows that the proposed interval type-2 fuzzy logic system improves performance in coiler entry temperature prediction under the tested condition.

[1]  M. Schlang,et al.  Neural networks for steel manufacturing , 1996 .

[2]  Jerry M. Mendel,et al.  Back-propagation fuzzy system as nonlinear dynamic system identifiers , 1992, [1992 Proceedings] IEEE International Conference on Fuzzy Systems.

[3]  M. Fujino,et al.  Application of fuzzy control system to hot strip mill , 1992, Proceedings of the 1992 International Conference on Industrial Electronics, Control, Instrumentation, and Automation.

[4]  Toshihiko Watanabe,et al.  A new mill-setup system for hot strip rolling mill that integrates a process model and expertise , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[5]  A. K. Tieu,et al.  Neural network application to head and tail width control in a hot strip mill , 1995, Proceedings of ICNN'95 - International Conference on Neural Networks.

[6]  Li-Xin Wang.  Fuzzy systems as nonlinear dynamic system identifiers. I. Design , 1992, [1992] Proceedings of the 31st IEEE Conference on Decision and Control.

[7]  Alberto Cavazos,et al.  Hot Strip Mill Temperature Prediction using Hybrid Learning Interval Singleton Type-2 FLS , 2003, Modelling and Simulation.

[8]  J. Mendel.  Uncertain Rule-Based Fuzzy Logic Systems: Introduction and New Directions , 2001 .

[9]  A. J. Morris,et al.  Fault detection in hot steel rolling using neural networks and multivariate statistics , 2000 .

[10]  Bong-Jin Yum,et al.  Robust design of artificial neural network for roll force prediction in hot strip mill , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[11]  Bart Kosko,et al.  Fuzzy Systems as Universal Approximators , 1994, IEEE Trans. Computers.

[12]  Hyung Suck Cho,et al.  A neural network approach to the control of the plate width in hot plate mills , 1999, IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339).

[13]  Barry N. Taylor,et al.  Guidelines for Evaluating and Expressing the Uncertainty of Nist Measurement Results , 2017 .

[14]  Jerry M. Mendel,et al.  Interval type-2 fuzzy logic systems , 2000, Ninth IEEE International Conference on Fuzzy Systems. FUZZ- IEEE 2000 (Cat. No.00CH37063).

[15]  Oscar Castillo,et al.  INTELLIGENT CONTROL OF DYNAMIC SYSTEMS USING TYPE-2 FUZZY LOGIC AND STABILITY ISSUES , 2006 .