The Thickness-Control Of Gypsum-Fibre Board Base On Intelligent Control

Recent years, the industry of Gypsum-Fibre board has developed at full speed in our country, and our country has become one of the countries whose output of Gypsum-Fibre board is most in the world. But the industry of gypsum material starts very late in our country, especially in Gypsum-Fibre board, so currently sizable disparity exists in our country's Gypsum-Fibre board compared with international advanced level. The board thickness is main quality target in production of Gypsum-Fibre board. So the technology of thickness control has become pivotal point in modern pressing machine. As far as the technique is concerned, the key scientific problem in the world is thick forecast and the realization of thick intelligent control.The pressing machine system is a basic part, its dynamic characteristic and steady characteristic affect the capability of entire thick control system. There are many influencing factors in the process of pressing Gypsum-Fibre board, such as machine running rate,height of stuff,main roller pressure,one area pressure , two area pressure and three area pressure. But these relations have the characteristics of nonlinear and the time-variable. So it is difficult to express the relation of parameter and dynamic characteristic using traditional modeling way. Therefore, this way has the difficult to establish mathematics model of thickness, and the precision of prediction cannot satisfy the thick on-line control request. This paper established prediction model of pressing machine based on Elman dynamic recursion network algorithms to apply flatness online prediction for enhance the steady state performance and real time performance of the system in thick control process. The simulation results indicate that this network model may basically reflect the corresponding relations of actual model's input and output.Finally aiming at the characteristics of nonlinear,time-variable and lag, this paper has designed a fuzzy controller base on genetic algorithms. CA is employed to optimize the membership functions of the Fuzzy Logic Controller .This way overcomes the insufficiency of traditional way. The simulation results indicate that the thickness of board can be efficiently controlled, the FLC optimized by GA has better control performance than conventional FLC and it has self-adaptive capability to a certain degree.