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Research On Modeling And Control Of 1500 Temper Rolling Mill Force System

Posted on:2010-01-20Degree:MasterType:Thesis
Country:ChinaCandidate:L LiFull Text:PDF
GTID:2121360302459108Subject:Mechanical and electrical engineering
Abstract/Summary:PDF Full Text Request
The temper rolling is the last working procedure which produces high quality thin strips and ensures product quality.The research work of this thesis is based on a 1500 temper rolling of some company. Considering dynamic performances of hydraulic control systems, The 1500 temper rolling`s mechatronic hydraulic model is established. Through analyzing hydraulic system composition and control strategies, the mathematical model was improved. The mechatronic hydraulic model provide foundations to improve and optimize control strategies of HAGC system.According to the project`s requirement, a data acquisition and test (DAQT) system based on virtual instruments technology is designed. With this DAQT system data in industrial process of the 1500 temper rolling is collected. After pretreating the acquired data, use least square method to identify system parameter model, a more accurate model is acquired.According to the identify mathematical model of force control system of the temper rolling, the PID controller is designed, and satisfying control effect has been achieved in hydraulic rolling force conctrol system. Because of the design requirements of rapid response, high control precision and anti-interference of the rolling force control system, A BP neural network adaptive PID controller is designed. The simulation results indicate satisfactory dynamic characteristics of the controller. Because the weights of the neural network correspond with the PID parameters, the adaptive and anti-interference ability of the controller is satisfying.There are certain instructional meaning and reference value for future research in temper rolling in the research achievements of this thesis.
Keywords/Search Tags:Temper rolling, Wavelet analysis, System identification, PID parameter self-tuning, BP neural network
PDF Full Text Request
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