Study On Intelligent And Predictive Control Of Cold Rolling Mill

Generalized Predictive Control(GPC) is , which appeared in 80's, a new type of computer control method and one of the most representative algorithm. It has received increasing attention in the field of control and industry. Based on lots of literature, this paper introduces fundamental principles of predictive control and the newly developments of GPC.This paper studies the basic algorithm and characteristic of GPC. All the algorithms of GPC in Chapter II have to solve Diophantine equation and the inversion of big matrix online. So real time control is infeasible. In order to solve this problem, two kinds of resolutions are put forward in Chapter III.Based on the traditional predictive control, it has provided new predictive and intelligence control methods mixing predictive and neural network. The research results show that by adopting parallel proceeding structure on a large scale, neural network has high convergence coefficients irrelevant to the number of nodes. For this reason, neural network can resolves the GPC's problems of modeling and rapidness. But in some nonlinear system there is a error which can't decrease with the neural network modeling. So a robust GPC model is presented, which can cut down the sensitivity of the identifier and controller, increase the adaptive ability and robustness of the controller.The paper makes the full research from control theory, design method and the application of the theory to four fight cold-rolling mill's tension control. A PIGPC model based on Elman network is presented in this paper. This algorithm not only can improve the characteristic of robust but decrease the control time, and can max the application of industrial.Finally the application of GPC to the tension control of cold trandom machine is studied. Different design solutions of GPC mentioned above are used to realize expectation.