An Expert System in Automatic Gauge Control for Rolling Process

Beacuse the practical mathematic model of rolling process can't be built accurately,this paper established an expert system to control the rolling steels' gauge by adjusting the setup roll open, which combined the experience of theoreticians and operators. The system applied the expression method of rule-skeleton+rule-body', and selected an appropriate non-exact reference model and self-study algorithm. The whole system, including auxiliary routes, is designed in Borland C++. Some experiments on this system have been done, and a good result has been achieved.

Monitor automatic gauge control strategy with a Smith predictor for steel strip rolling

The simplified transfer function diagram block for a monitor automatic gauge control （Mon-AGC） system of strip steel rolling process was investigated. The new notion of strip sample length was given. In this way, the delay time varying with the rolling speed was evaded. After a Smith predictor was used to monitor the AGC system, the control laws were deduced for both proportional and integral regulators. The control strategies showed that by choosing the controller parameter P=∞ for both control algo- rithms each regulator could compensate the whole strip gage error in the first control step. The result shows that the integral algo- rithm is more controllable for the system regulating process and has a better steady-state precision than the proportional regulator. Compared with the traditional control strategy, the new control laws have a faster response speed and a hieher steadv-state precision.

Improved Smith prediction monitoring AGC system based on feedback-assisted iterative learning control

The performance of Smith prediction monitoring automatic gauge control(AGC) system is influenced by model mismatching greatly in strip rolling process. Aiming at this problem, a feedback-assisted iterative learning control strategy, which learned unknown modeling error by using previous control information repeatedly, was introduced into Smith prediction monitoring AGC system. Firstly, conventional Smith predictor and improved Smith predictor with PI-P controller were analyzed. Secondly, on the basis of establishing of feedback-assisted iterative learning control strategy for improved Smith predictor, process control signal update law and control error were deduced, then convergence condition of this strategy was put forward and proved. Finally, after modeling the automatic position control system, the PI-P Smith prediction monitoring AGC system with feedback-assisted iterative learning control was researched through simulation. Simulation results indicate that this system remains stable during model mismatching. The robustness and response of monitoring AGC is improved by development of feedback-assisted iterative learning control strategy for PI-P Smith predictor.

Dynamic Optimization of Feedforward Automatic Gauge Control Based on Extended Kalman Filter

Automatic gauge control is an essentially nonlinear process varying with time delay,and stochastically varying input and process noise always influence the target gauge control accuracy.To improve the control capability of feedforward automatic gauge control,Kalman filter was employed to filter the noise signal transferred from one stand to another.The linearized matrix that the Kalman filter algorithm needed was concluded;thus,the feedforward automatic gauge control architecture was dynamically optimized.The theoretical analyses and simulation show that the proposed algorithm is reasonable and effective.

Adaptive Modification of the Rolling Force Prediction

In rolling process, the rolling force is an important parameter. The precision of the predicted rolling force will directly affect the precision of the finished product. By using adaptive control theory and fusing the measured and predicted data, the precision of the predicted rolling force is gradually improved. This system has been used in plant for more than one year, and the result of the application shows that the system has steady and reliable performance, and high precision.

The Techniques to Reduce Variations within One Plate

Conventional techniques to control variations within one plate have been based on preset models and constant automatic position control (APC or pressure feedback automatic gauge control(PAGC).However.because of the rolling force prediction error in the preset models and of the inadequate response speed of dynamic system and of the eccentricity, etc.,the conventional method has not given satisfactory results, the statistics'variations within one plate are in range of 0.25-0.60 mm The authors have developed the techniques to control the variations, which are dynamic intelligent control of hydraulic screwdown system. synchronism control of hydraulic screwdown, eccentricity control method by rotary encoder and the curve of modulus of mill measured automatically, etc., The techniques were fully and successfully industrialized in The Plate Mill of Maanshan Iron and Steel Company and good results that variations are in range of 0.08-0.15min hare been obtained in that mill.

Algorithm design and application of novel GM-AGC based on mill stretch characteristic curve

As the spring equation is limited to the accuracy of mill stiffness and the linearity of the mill spring curve, the traditional gaugemeter automatic gauge control(GM-AGC) system based on spring equation cannot meet the requirements of practical production. In allusion to this problem, a kind of novel GM-AGC system based on mill stretch characteristic curve was proposed. The error existing in calculating strip thickness by spring equation were analyzed first. And then the mill stretch characteristic curve which could effectively eliminate the influence of mill stiffness was described. The novel GM-AGC system has been applied successfully in a hot strip mill, the application results show that the thickness control precision is improved significantly, with the novel GM-AGC system, over 98.6% of the strip thickness deviation of 3.0 mm class can be controlled within the target tolerances of ±20 μm.

Influence of mill modulus control gain on vibration in hot rolling mills

The rolling mill vibration is characterized by the coupling effects among mechanical,electrical,hydraulic and interfacial subsystems.The influence of the mill modulus control gain in automatic gauge control on the vibration in hot rolling mills was investigated.Firstly,an experiment related to the mill modulus control gain was carried out in the hot rolling mill process,and it was found that the rolling mill vibration increases with the mill modulus control gain.Then,based on the Sims rolling force method,the coupling dynamic model was established to explain this phenomenon.Finally,the influence of mill modulus control gain on the vibration was analyzed numerically on the basis of the coupling dynamic model.Moreover,the agreement between the experimental results and the simulation results was confirmed and the measure reducing the mill modulus control gain was obtained to relieve mill vibration.