Applied Research On Intelligence Fuzzy Control System Of The Copperplate Rolling

In the production of the copperplate rolling, the fluctuation with thickness of supplied materials, the disparity of the material performance, the deviation of actually used apparatus, external interference and apparatus change of spare part, etc. can not make traditional PID control set up accurate mathematical model, reach the ideal control result. Thus, the precision of copperplate products can't meet the demands far away. On the contrary, fuzzy control don't depend on the mathematical model of controlled objects and have the ability to adapt to the changes of system parameters, so it has the potential to overcome the drawbacks of conventional control schemes. The paper is developed under such engineering background.Fuzzy control is a kind of computer numerical control based on fuzzy set theory, fuzzy linguistic- variable and fuzzy logical reasoning, in which the experience of skilled operators and the knowledge of experts are summed up into the control rules in the form of "IF ... THEN...", that is fuzzy logical sentence, and then the control results can be attained by fuzzy logical reasoning. This procedure is similar to the ideation of human being.In this thesis, according to changeable quantity, nonlinear and time-varying of the copperplate rolling process, the simple fuzzy controller was first designed based on the analysis of the fuzzy control theory, which is the basis of other high-level controllers. This kind of controller is easy to design and operate, and has improved convergence rates and less overshoot than PID controller, but has stable error. In order to improve the properties of the fuzzy controllers, fine-tune-rule fuzzy controller, fine-tuned parameter fuzzy controller and fuzzy-PI controller were designed respectively based on the simple fuzzy controller. Then fuzzy logic inferential system is established by using toolbox of fuzzy logic in Matlab7.0.Secondly, in Simulink6.0, through the instance of the template of S-function, the module of S-function for optimizing fuzzy control and corresponding control model are constructed, and the parameters of simulation are set. Finally, the dynamic simulation of the system is implemented.At last, the simulation curve was given, which show that the desired results were attained.