Adaptive Fuzzy Neural Network of Cast-Rolling Hydraulic AGC

Abstract:

Article Preview

Cast-roll hydraulic AGC is the main control means of the strip t hickness. The control effect of traditional PID is poor for adjusting this kind of model parameters. Therefore, a new type of fuzzy neural network self-learning and adaptive controller is proposed, and analyze the composition and basic performance. The simulation results show that the new controller can effectively improve its response, what’s more it has a better dynamic performance more than another control strategies.

Info:

Periodical:

Key Engineering Materials (Volumes 531-532)

Edited by:

Chunliang Zhang and Liangchi Zhang

Pages:

773-777

Citation:

Z. C. Wang et al., "Adaptive Fuzzy Neural Network of Cast-Rolling Hydraulic AGC", Key Engineering Materials, Vols. 531-532, pp. 773-777, 2013

Online since:

December 2012

Export:

Price:

$38.00

[1] L. Hu, L.G. Zhao, Z.H. Duan, H.Y. Zhao and D.Y. Ju: Correlative Technologies of Twin-roll Casting Technics of Thin Strip. Steel Rolling, Vol. 21 (2004) No. 1, pp.34-37(In Chinese).

[2] L.X. An, H.F. Wang and Z.W. Yang. Control Model and Function Application of Mass Flow AGC. Electric Drive, Vol. 45 (2009) No. 7, pp.41-45(In Chinese).

[3] Keum-shik Hong, Jeom-Goo Kim, Masayoshi Tomizuka. Control of strip casting process decentralization and optimal roll force control. Control Engineering Practice. Vol. 9 (2001) 933-945.

DOI: https://doi.org/10.1016/s0967-0661(01)00042-9

[4] S.H. Jiang and D.G. Shen. A Kind of PID Controller Based on Fuzzy Neural Network. Journal of Anqing Teachers College, Vol. 10 (2004) No. 2, pp.1-4(In Chinese).

[5] S.B. Tan, Y.F. Zhong, J.C. Liu and X.H. Xu. Modeling and Simulation of Rolling Gap Control in Strip Mills. Journal of System Simulation, Vol. 18(2005) No. 6, pp.1425-1427(In Chinese).

[6] J. Zhao and J.J. Chen. Design of the Fuzzy neural PID controller based on hybrid PSO. Journal of Xidian University. Vol. 35 (2008) No. 1, p, 54-59(In Chinese).

[7] J.K. Liu: Advanced PID control and MATLAB simulation(Publishing House of Electronics, China, 2004)(In Chinese).

[8] K.J. Hunt, D. Sbarbaro, R. Zbikowski and P.J. Gawthrop. Neural networks for control systems-A survey. Automatica, Vol. 28 (1992) No. 6, pp.1083-1112.

DOI: https://doi.org/10.1016/0005-1098(92)90053-i

[9] Wu Zhi Qiao and Masaharu Mizumoto. PID type fuzzy controller and parameters adaptive method. Fuzzy Sets and Systems, Vol. 78 (1996) No. 1, pp.23-25.

DOI: https://doi.org/10.1016/0165-0114(95)00115-8