Intelligence Robust Theory And It's Application In Tension Control Of Cold-Rolling Mill

Neural Networks, an embranchment of nonlinear control which does not dependent on mathematic model, is adapt to controlled systems of uncertainties and highly nonlinear. Robust control theory fit systems with parameters disturbances or outside perturbations especially the problem when uncertainties have influences on system performance. The paper made deep research of fundamental, development and controller design of Robust control and Neural Networks based on Intelligence Robust control method and made simulation to the results.The paper made the full research from control theory, design method and the application of the theory to four fight cold-rolling mill's tension control. First, the math model of tension tache has been deduced in detail and we analyzed the selection of parameters, which is part of the model by which we reached the conclusion that there are two variable parameters in tension control tache.Second, a self-study fast BP algorithm is purposed in the paper considering that cold-rolling mill's tension control is a fast time-critical system. Two design methods of controller were purposed too in this paper. The first is based on Robust control. The solution of two Riccati equation is conversed into linear matrix inequality (LMI) by which the problem that there exists coupling between two Riccati equations and depending on parameter adjusting is solved. The second is connecting Neural Networks with control theory. In virtue of the nonlinear approaching ability of Neural Networks, the control problem was solved with uncertainties such as nonlinear disturbances.Besides, the design method purposed by the paper was applied to the tension control of four fight cold-rolling mill and made simulation for it. At last, the paper introduced the fundamental of FCS and design distributed control system based on FCS in four fight cold-rolling mill's tension control.