Aiming at the problem that the measured value of coiling temperature(CT)fluctuates greatly and the CT feedback con-trol and model self-learning function cannot be put into normal operation during the low temperature coiling of thick gauge high strength steel in a 2 150 mm hot rolling production line,a new combined filtering algorithm based on limiting filtering method and Kalman filtering method was developed.Three typical combination algorithms were compared and analyzed.Compared to the Limiting Weighted Smooth Filtering and Limiting Recursive Average Filtering algorithms,it was found that the Limiting Kalman Filtering algorithm improved the authenticity and reliability of CT measurements while ensuring good robustness in noisy envi-ronments.The Limiting Kalman Filtering algorithm was embedded into the existing laminar cooling control system,which real-ized the accurate measurement of the CT values under the low temperature coiling condition and ensured the normal input of the CT control function.When the optimized control system was used to produce X80 pipeline steel,the actual CT values can be controlled within the target temperature of±30 ℃,and good application results were achieved.
关键词
厚规格高强钢/低温卷取/卷取温度/限幅卡尔曼滤波/限幅加权平滑滤波/限幅递推平均值滤波
Key words
thick gauge high strength steel/low temperature coiling/coiling temperature(CT)/Limiting Kalman Filtering/Lim-iting Weighted Smooth Filtering/Limiting Recursive Average Filtering
维普
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