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

Abstract

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 Hα 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 Hα norm reaches 2.989. These levels are acceptable for the monitoring AGC system.