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

作     者：张浩宇 孙杰 张殿华 陈树宗 张欣 

作者机构：State Key Laboratory of Rolling and Automation Northeastern University 

出 版 物：《Journal of Central South University》 (中南大学学报（英文版）)

年 卷 期：2014年第21卷第9期

页      面：3492-3497页

核心收录：

学科分类：0810[工学-信息与通信工程] 0806[工学-冶金工程] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0703[理学-化学] 0802[工学-机械工程] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 080201[工学-机械制造及其自动化] 

基　　金：Project(51074051)supported by the National Natural Science Foundation of China 

主　　题：automatic gauge control Smith predictor monitoring automatic gauge control (AGC) feedback-assisted iterativelearning control automatic position control 

摘      要：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.