scholarly journals Molten Steel Level Control of Strip Casting Process Monitoring by Using Self-Learning Fuzzy Controller

10.5772/14075 ◽  
2011 ◽  
Author(s):  
Hung-Yi Chen ◽  
Shiuh-Jer Huang
Keyword(s):  
Process Monitoring ◽  
Molten Steel ◽  
Fuzzy Controller ◽  
Casting Process ◽  
Strip Casting ◽  
Level Control ◽  
Self Learning

scholarly journals Robust Molten Steel Level Control in a Strip-casting Process

2005 ◽  
Vol 45 (8) ◽  
pp. 1165-1172 ◽  
Author(s):  
D. S. LEE ◽  
Jin S. LEE ◽  
T. KANG
Keyword(s):  
Molten Steel ◽  
Casting Process ◽  
Strip Casting ◽  
Level Control

Sliding-Mode Fuzzy Control Using in a Double-Axial Pump-Controlled Folding Machine

2013 ◽  
Vol 418 ◽  
pp. 88-91 ◽  
Author(s):  
Ray Hwa Wong
Keyword(s):  
Fuzzy Controller ◽  
Sliding Mode ◽  
Fuzzy Rule ◽  
Coupled System ◽  
Control Performance ◽  
Level Control ◽  
Axial Pump ◽  
Strong Robustness ◽  
Adaptive Laws ◽  
Self Learning

The double-axial pump-controlled folding machine is a coupled system and has significant structural interaction. This paper proposes a coupled adaptive self-organizing sliding-mode fuzzy controller (CASOSMFC) to improve its level control performance. CASOSMFC is associated with two basic single-axial sliding-mode fuzzy controllers, self-learning fuzzy rule mechanisms, adaptive laws, coupled controllers and regulators. Different loadings are used to simulate different working pieces and variety coupled intensity folding processes. Experimental results indicate that CASOSMFC applied in the folding machine has excellent level control performance and has strong robustness to different coupled intensity.

Application of GA-FNC in Crystallizer Liquid Level Control System

2011 ◽  
Vol 199-200 ◽  
pp. 1777-1780
Author(s):  
Ming Li ◽  
Da Yong Yang
Keyword(s):  
Control System ◽  
Control Strategy ◽  
Steel Casting ◽  
Fuzzy Controller ◽  
Liquid Level ◽  
Level Control ◽  
Intelligent Control System ◽  
Casting System ◽  
The Stability ◽  
Self Learning

This article discusses the control strategy of the steel casting system which possesses the characteristics of large inertia, time-varying and nonlinear. Aiming at getting the minimum deviation of liquid level, the control strategy uses the genetic algorithm to off-line optimize the parameters (cij,bj) of the Gaussian membership function and the network structure of fuzzy controller which affect the overall system firstly. Then, BP algorithm is used to online regulate and optimize the weight parameters of the control output which affect the system partly. Finally, the intelligent control system of the liquid level which is based on GA-FNC is simulated. The results show that the method can enhance the ability of self-learning and robustness of the system greatly and improve the stability of steel casting system significantly.

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