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
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 r...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.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.
文摘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.
