Study On Cooperative Optimization Strategy Of Gauge-Looper Integrated Control System For Hot Strip Mills

With the development of modern industry,the demand for strip control accuracy of tandem hot mill is becoming critical.The control strategy about thickness and looper is key research of rolling automation.After years of development,the automation system of hot strip mills has already achieved a very high level.However,the control effect of thickness and looper based on traditional control theory has already approached the limit,it is very difficult to meet the increasingly strict requirement for hot rolled strip steel.Therefore,for the further improvements of control performance,it is necessary to introduce advanced control strategies into the control system of hot strip mills.Based on the 1700mm tandem hot mill as the reasearch object,state-space expression of tandem hot mill was established,and the study was carried out on rolling characteristics.For the system has better control effect and stronger stability,The advanced control strategies,such as Sliding mode variable structure control?Model predictive control were introduced into gauge and looper control system.The main work is as follows:(1)The mian functions and data communication methods of computer control system for hot strip mill were analyzed in detail.The basic equations for strip rolling process,such as mass flow equation,tension equation and so on were studied,and the mathematical models for strip rolling process,such as temperature drop model,rolling force model and so on were analyzed.The high order polynomial mill stretch and looper length equations which are suitable for practical engineering were established.(2)Combining with the basic equations for strip rolling process,the incremental models used for rolling characteristic analysis were deduced,and the coefficient matrixs of state-space expression were established.The rolling characteristics were studied based on state-space analysis method.The dynamic change process of strip gauge,interstand tension and looper angle when external disturbance or regulation changes was simulated.The influence law of external disturbance and regulation on system outputs was obtained by analyzing the dynamic change process.Based on the practical production data of the hot strip mills,the control effect of traditional gauge and looper control system and its existed limitation were studied and analyzed.(3)The theoretical model of hydraulic gap control(HGC)system was established,and the order of established model was reduced.The model parameters of HGC system were obtained by system identification.Based on the identified model,the conventional Smith predictive strategy was designed for monitoring AGC system.In order to improve the robustness of system,the sliding mode variable structure control(SMC)controller was introduced into Smith prediction monitoring AGC system.The traditional reaching law was replaced by the double power reaching law in the SMC controller design,the chattering phenomena produced by SMC controller can be weaken effectively by the double power reaching law.The results show that both control systems can obtain good control effect with model matching;when the model mismathced,the Smith prediction monitoring AGC system based on SMC controller has better robustness.(4)By analyzing the dynamic coupling process of looper height system and strip tension system,a dynamical mathematical model of a looper system was established.Based on the established model,a double-input and double-output looper system was decomposed to two double-input and single-output systems.The controllers were designed with dynamic matrix control strategy(DMC)for looper height system and strip tension system,respectively.The cooperative optimization control for looper height and strip tension was realized by selecting appropriate quadratic performance index.The results show that the designed DMC control system is not sensitive to accuracy of the controlled object model.When there are model parameters perturation or outside interference,the looper system based on DMC controller still has good control effect.(5)By combining the state equations of gauge system ans looper system,the state-space model of gauge-looper integrated system was established.The controller design method based on inverse linear quadratic control strategy(ILQ)for this model was presented.According to the expected dynamic performance indexes of gauge-looper integrated system,the reasonable expected closed-loop poles can be obtained.The state feedback control was introduced into integrated system for realizing the expected pole assignment.The selection process of eigenvectors was optimized for practical application,and the theory algorithm of ILQ was improved.The controller based on improved ILQ control theory was introduced into gauge-looper integrated system.By cooperative optimal control the gauge?tension and angle,the problem that gauge and looper systems were treated as two independent subsystems,and the coupling between two subsystems which can not be cooperative controlled in the traditional method were solved.(6)The gauge-looper integrated system was discretized as actual sampling period,the discrete state-space model of gauge-looper integrated system was obtained,and the discrete model predictive controller based on augmented minimal state space(MPC)was designed.By adjusting the weighting matrix in performance index function of MPC controller,the dynamic response of strip gauge?tension and looper angle were cooperative controlled.For improving robustness of integrated system,the actual system outputs were introduced into feedback correction for correcting the predictive outputs,and the performance index function was rolling optimized.The results show that when integrated systems are affected by external disturbances,ILQ and MPC controller can cooperative optimal system outputs;when there are model parameters perturation,MPC controller which is designed based on rolling optimization and feedback correction strategy requires low in the model precision,the average fluctuation of outputs are close to that with model matching,MPC controller has the stronger robustness than ILQ and PI controllers.