Neural Network Based On The Amount Of Roll Bending Leveler Adaptive Control System

Because the plates are affected by many factors during the heating, rollingand cooling process,the rolled plates have shape defects, such as wave in thepart of plates,waves in the edge of plates,buckling. In order to eliminate defects,the roller must be equipment the bending parts. The bending system of the plateroller leveler is a key part which decides sheet quality and improves thestraightening efficiency. Because the straightening process is a multivariable,nonlinear, slowly time-varying, and strong coupling process. The model usedconventional techniques does not involve time-varying and coupling factors.Therefore, the paper combines the Existing model of bending value with neuralnetwork to construct the bending amount of models and neural networktechnology combining construct the adaptive model of bending value, whichmakes that have adaptive capacity and the ability of learning by itself. Thispaper designs a neural network as a bending roll control model. This articlecontains the following sections:(1) The design and implementation of bending value sample valuemanagement system. The creation's background of this part is the monitoringsystems of11-high hydraulic powerful leveler of a factory. The two stepmonitoring system is consisted of the automation station, the main operatorstation, the main monitoring station, the engineer station and server clusterswhich together constitute the distributed computer control system. The papercreates the management system of sample value depend on the system. Thisprocedure interface owns the ability of managing sample value, communicationdata and real-time data, querying sample value from the sample table accordingto certain conditions and adding,deleting sample value in sample table.(2) The design and implementation of the adaptive model of Bending value.The part creates a intelligent roll bending model with adaptive function which ismade up of analytical mathematical model and neural network. The neuralnetwork model makes the impact of historical data and standard samples to thesystem imported into the model to improve the accuracy of the model and ability to adapt. After many experience, it designs the learning function, the transferfunction, the training function, and the number of neurons in the hidden layer ofneural network to make it own fast learning speed, high accuracy andgeneralization.(3) The application of the adaptive model of bending value. Using thedatabase technology, OPC technology, the industrial field-bus technologycombines the adaptive model of bending value with the monitoring system ofstraightening machine and the basic automation system, so that build acomplete process system of straightening machine. The system commands theleveler monitoring system to control the PLC by OPC, and then the PLCcontrols the roller and operate the bending rollers to improve the plate. Thesystem is applied in the control system of the large third-generation fullhydraulic plate straightening machine.