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
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.
文摘近年来,AIS(automatic identification system)的迅速发展极大地推动了海上船舶的时空动态监测。我国自行设计的卫星AIS每天可接收大量数据,为掌握全球渔船作业动态提供条件。针对国家卫星海洋应用中心接收的卫星AIS数据,首先通过编程实现数据下载,将所有FTP(file transfer protocol)服务器端的AIS数据保存到本地磁盘;其次,按照AIS数据的消息类型对其分组,根据消息格式筛选出包含有日期、时间、船舶唯一标识符、经度、纬度、航速和航向等核心信息的有效数据类型;再次,从有效数据类型中按照船舶唯一标识符将所有有效船舶划分为“渔船”“非渔船”2类,对其进行统计与分析;最后,按照渔船位置信息绘制专题热力图,直观地展示渔船空间分布情况。结果显示,该方法能够以脚本自动化方法实现卫星AIS数据下载、分类、分析和自动制图。自动化制图能直观展示渔船位置分布情况,降低人工成本,为渔业部门的生产管理决策提供数据参考。
