Iterative Learning Control with an Improved Internal Model for a Monitoring Automatic-Gauge-Control System

被引:1
作者
Yin Fang-chen [1 ]
Zhang Dian-hua [1 ]
Xu, Li [1 ]
Jie, Sun [1 ]
机构
[1] Northeastern Univ, State Key Lab Rolling & Automat, Shenyang 110819, Peoples R China
关键词
tandem hot mill; monitoring automatic gauge control; hydraulic gap control system identification; particle swarm optimization; internal model control; iterative learning control; LONG DEAD-TIME; SMITH PREDICTOR; IMPROVED PSO; IMC DESIGN; IDENTIFICATION; PARAMETERS;
D O I
10.1007/s11015-016-0205-y
中图分类号
TF [冶金工业];
学科分类号
0806 ;
摘要
The long time delay in the monitoring automatic gauge control (AGC) of strip rolling by a tandem hot mill adversely affects system stability. To solve this problem, internal model control (IMC) and iterative learning control were applied to a monitoring-AGC system. A mathematical model of the hydraulic gap control system was established focusing on the seventh stand of a 1450-mm tandem hot mill in a factory. Model parameters were identified employing a particle swarm optimization algorithm. Using the identified hydraulic gap control model, a monitoring AGC system with an improved internal model (IIMC-MNAGC) and an iterative-learning-control strategy for an improved-internal-model monitoring AGC system (ILC-IIMC-MNAGC) were established. Finally, simulation experiments for IIMC-MNAGC and ILC-IIMC-MNAGC were conducted using MATLAB/Simulink software. The simulation results show that for the IIMC-MNAGC system, when the model matches, the rising time reaches 43.6 msec, the overshot reaches 4.34%, the integral square error (ISE) reaches 0.0131, and the H-alpha norm reaches 2.953. These levels are acceptable for the MN-AGC system. When there is model mismatch due to the inaccuracy of the pure delay, for the IIMC-MNAGC system, the rising time increases to 263.5 msec and the overshot increases to 36.7%, which are unacceptable for the monitoring AGC system. When there is model mismatch for the ILC-IIMC-MNAGC system, the rising time reaches 38.9 msec, the overshot reaches 1.37%, the ISE reaches 0.0095, and the H-alpha norm reaches 2.989. These levels are acceptable for the monitoring AGC system.
引用
收藏
页码:987 / 997
页数:11
相关论文
共 21 条
[1]   A NEW SMITH PREDICTOR FOR CONTROLLING A PROCESS WITH AN INTEGRATOR AND LONG DEAD-TIME [J].
ASTROM, KJ ;
HANG, CC ;
LIM, BC .
IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 1994, 39 (02) :343-345
[2]   Neural identification of dynamic systems on FPGA with improved PSO learning [J].
Cavuslu, Mehmet Ali ;
Karakuzu, Cihan ;
Karakaya, Fuat .
APPLIED SOFT COMPUTING, 2012, 12 (09) :2707-2718
[3]   Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory [J].
Chen, Chen ;
Xiong, Zhihua ;
Zhong, Yisheng .
CHINESE JOURNAL OF CHEMICAL ENGINEERING, 2014, 22 (07) :762-768
[4]   Robust multivariable control for hot strip mills [J].
Hearns, G ;
Grimble, MJ .
ISIJ INTERNATIONAL, 2000, 40 (10) :995-1002
[5]  
Jin Q. B., 2006, ISA T, V53, P713
[6]   Partitioned model-based IMC design using JITL modeling technique [J].
Kalmukale, Ankush Ganeshreddy ;
Chiu, Min-Sen ;
Wang, Qing-Guo .
JOURNAL OF PROCESS CONTROL, 2007, 17 (10) :757-769
[7]   Obtaining controller parameters for a new PI-PD Smith predictor using autotuning [J].
Kaya, I .
JOURNAL OF PROCESS CONTROL, 2003, 13 (05) :465-472
[8]   A new Smith predictor and controller for control of processes with long dead time [J].
Kaya, I .
ISA TRANSACTIONS, 2003, 42 (01) :101-110
[9]   Improving performance using cascade control and a Smith predictor [J].
Kaya, I .
ISA TRANSACTIONS, 2001, 40 (03) :223-234
[10]   Robust PID tuning for Smith predictor in the presence of model uncertainty [J].
Lee, D ;
Lee, M ;
Sung, S ;
Lee, I .
JOURNAL OF PROCESS CONTROL, 1999, 9 (01) :79-85