Abstract
The twin-roll strip casting process is a steel-strip production method which combines continuous casting and hot rolling processes. The production line from molten liquid steel to the final steel-strip is shortened and the production cost is reduced significantly as compared to conventional continuous casting. The quality of strip casting process depends on many process parameters, such as molten steel level in the pool, solidification position, and roll gap. Their relationships are complex and the strip casting process has the properties of nonlinear uncertainty and time-varying characteristics. It is difficult to establish an accurate process model for designing a model-based controller to monitor the strip quality. In this paper, a model-free adaptive neural network controller is developed to overcome this problem. The proposed control strategy is based on a neural network structure combined with a sliding-mode control scheme. An adaptive rule is employed to on-line adjust the weights of radial basis functions by using the reaching condition of a specified sliding surface. This surface has the on-line learning ability to respond to the system’s nonlinear and time-varying behaviors. Since this model-free controller has a simple control structure and small number of control parameters, it is easy to implement. Simulation results, based on a semiexperimental system dynamic model and parameters, are executed to show the control performance of the proposed intelligent controller. In addition, the control performance is compared with that of a traditional PID controller.
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This paper was recommended for publication in revised form by Associate Editor Kyung-Soo Kim
Hung-Yi Chen received his M.S. degree from Auburn University, AL., USA and his Ph.D. degree from National Taiwan University of Science and Technology, Taiwan, R.O.C., in 1991 and 2006, respectively, both in Mechanical Engineering. Since 1997, he has been with Mingchi University of Technology, where he is currently an associate professor. His research interests include intelligent control applications, mechatronics, automation, and machine tool control.
Shiuh-Jer Huang received his B.Sc. degree from National Cheng-Kung University, Tainan, Taiwan, in 1978, his M.Sc. Degree from National Taiwan University, Taipei, Taiwan, in 1980, and his Ph.D. degree from the University of California, Los Angeles, USA, in 1986, all in Mechanical Engineering. In 1986, he joined the faculty of the Department of Mechanical Engineering, National Taiwan University of Science and Technology in Taipei, Taiwan, where he is currently a professor. His research interests are in vehicle active suspension systems, robotic system analysis and application control, factory automation, and machine tool control.
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Chen, HY., Huang, SJ. Adaptive neural network controller for the molten steel level control of strip casting processes. J Mech Sci Technol 24, 755–760 (2010). https://doi.org/10.1007/s12206-009-1212-8
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DOI: https://doi.org/10.1007/s12206-009-1212-8