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| Roll eccentricity compensation control based on iterative learning |
| QIAO Ji-zhu1, SHI Hong-jian2, SUN Jie1, PENG Wen1, LI Xiao-jian3, ZHANG Dian-hua1 |
1. State Key Laboratory of Rolling and Automation, Northeastern University, Shenyang 110819, Liaoning, China;
2. Equipment Department, Ningbo Baoxin Stainless Steel Co., Ltd., Ningbo 315800, Zhejiang, China;
3. College of Information Science and Engineering, Northeastern University, Shenyang 110819, Liaoning, China |
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Abstract Aiming at roll eccentricity in rolling mill, an iterative learning control method which was widely used in industrial robot repeatability control was applied to roll eccentricity compensation control by combining the characteristics of eccentricity signal and compensation control method. On the basis of learning the cycle error data, the adjustment of rolling mill eccentricity control system was more accurate. The test results of constructed roller eccentricity signal show that compared with the fast Fourier transform compensation method, the compensation accuracy is improved by 20.1%-56.4% after 10-50 iteration learning. After iterative learning law parameters are optimized, the compensation accuracy reaches more than 50% after iterative learning, and the convergence speed is greatly improved. Considering that the frequency of eccentricity signal changed when the rolling mill was accelerating and decelerating, the frequency conversion signal was compensated by iterative learning control. After 10 times of fast learning compensation, the value of error evaluation function reached 13.6% of initial. The actual production line data of one pass in continuous cold rolling are used for testing, and the results show that the iterative learning compensation control can effectively improve the accuracy of eccentricity compensation after learning. Study results based on the iterative learning control method provides some reference for the compensation control of roll eccentricity.
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Received: 19 December 2022
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2023, Vol. 33 
