Research On Flatness Forecasting Model And Generialize Pridictive Control Algorithm For Cold Mill

In every department of national economy, plate and strip has extensive and important application. One of the quality indexes is flatness. The precision of plate and strip acquired by department is becoming more and higher. As a result, flatness control becomes the key technique and important develop direction for modern times high precise rolling mill. As far as the technique is concerned, theoretical basis and key scientific problem is the online flatness forecasting and advanced control method. In this study, author chooses flatness intelligent forecast and hydraulic roll-bending control as research object. The system of flatness measurement and control has been researched based on Generalized Predictive Control (GPC) and Artificial Neural Network (ANN). Main works are as follows:Flatness forecasting model is important to design flatness control system and precise flatness forecasting model is needed either in analyzing the control characteristic of the machines adjusted or in controlling online. In order to build a more fast and precise model of flatness forecast, an online distributed neural network flatness forecasting model is built based on product data. Diagonal recurrent neural network and fuzzy classification technique are introduced in the flatness forecasting model. A Correction Algorithm that used to forecasting model was set up, which cause model can adapt process changes and can obtain better forecasting result. The forecasting model overcomes a lot of defects, such as iterative operation, time-consuming operation, easily making dynamic model static model in many lay ahead feedback network, exploring a new non-parse method of building flatness model and resolving many problems in building complex system model.Due to non-linearity, time-variation and non-determinacy of hydraulic roll-bending control, as well the anti-interference request, a hydraulic roll-bending control scheme has been put forward based on direct GPC after analysis of system mathematical models, which is used to raise product ratio, make full use of hydraulic force and improve the dynamic performance of rolling mill system. GPC has been studied. According to the problem of big calculation amount caused by heavy and complicated matrix inversion, a new simplified iterative optimization algorithm has been presented to solve the GPC real-time problems caused by the amount calculation, which eliminate the matrix inversion of traditional GPC.Model feedback correction algorithm of GPC has Benn studied. Combining the RML and forgetting factor RLS, An improved RML parameter estimation method has been given. This method can modify model error in time to improve control precision and guarantee control effect. It can solve the problem that parameters estimation will become slow when the process parameters and noise signal are tightly coupled. The effect of time varying of systematic time lag and equipments capability for systematic stability and robustness has been effectively restrained.At last ,the paper further we discusses and assumes the application foreground of flatness online forecasting and GPC in flatness measure and control system, and shows their board development space and apllication foreground.