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 co...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.展开更多
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 th...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.展开更多
基金Project(51074051)supported by the National Natural Science Foundation of China
文摘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.
基金Project(51074051) supported by the National Natural Science Foundation of ChinaProject(N110307001) supported by the Fundamental Research Funds for the Central Universities,China
文摘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.
