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박철재 제어·로봇·시스템학회 2019 제어·로봇·시스템학회 논문지 Vol.25 No.4
In this paper, we propose a method of obtaining a stable winding shape by analyzing the dynamic characteristics of the hydraulic drive system and analyzing the control performance against disturbance in hot rolling mills. The model of servo position control system and accumulator is described. We also develop a model in the loop level simulator using a physical model for the analysis of the hydraulic drive system. The simulator is used to analyze the dynamic characteristics according to the capacity of the accumulator and to develop a method of selecting the appropriate capacity.
Dynamic Temperature Control with Variable Heat Flux for High Strength Steel
박철재 제어·로봇·시스템학회 2012 International Journal of Control, Automation, and Vol.10 No.3
We propose a dynamic temperature control (DTC) scheme for high strength steel to obtain the desired temperature and properties of steel in the run-out table (ROT) process. A control model with variable heat flux is developed to reduce the temperature deviation from the actual temperature of the strip, the temperature drop due to water cooling. The control concept uses field data and a time-temperature transformation (TTT) diagram. A ROT dynamic simulator (RoDys) with four simulation modes using the control model is developed to achieve the desired steel properties. The effectiveness of the proposed control scheme is verified from simulation results under a disturbance of the rolling speed. Using a hot strip mill field test, we show that the performance of the temperature control is sig-nificantly improved by the proposed control scheme.
박철재 제어·로봇·시스템학회 2018 제어·로봇·시스템학회 논문지 Vol.24 No.3
In this paper, we propose an algorithm to improve the accuracy of temperature control of the steel by optimizing the heat flux learning structure in the hot rolling cooling process. A modified two-sample t-test (MTST) method is suggested to overcome the problems of the two-sample t-test, and the t-test results of two groups are calculated with probability to confirm the identity of the mean. Based on the analysis of the proposed learning structure, we verify the algorithm using the actual data of the hot rolling mills. The test results show that the performance of temperature control is significantly improved by the proposed algorithm.