基于体积模型的烧结终点预测及模糊控制研究
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摘要
基于宝武集团梅山钢铁公司5#烧结机实际,设计基于体积(volume)模型的烧结终点位置自适应模糊控制器。首先,采用最小二乘法对烧结机两侧风箱废气温度进行拟合,建立烧结终点volume预测模型,通过计算两条曲线顶点与预设点之间构成的体积确定烧结终点位置。在此基础上,设计自适应模糊控制器,引入加权因子实现模糊规则自调整,并通过Matlab仿真验证。结果表明:烧结终点volume预测模型相比于传统模型更加准确;相比于传统PID控制器,基于volume模型的自适应模糊控制器不仅在快速性及超调量等方面表现更优,且具更强的鲁棒性。
An adaptive fuzzy controller based on volume model for sintering end point position was designed based on the actual situation of 5# sintering machine in Meishan Iron and Steel Company of Baowu Group. Firstly,the least squares method was used to fit the exhaust temperature of the bellows on both sides of the sintering machine, and the volume prediction model of the sintering end point was established. The sintering end point position was determined by calculating the volume between the two curve vertices and the preset points. Secondly, an adaptive fuzzy controller was designed to realize self-adjustment of fuzzy rules by introducing weighting factors, which was verified by simulation in Matlab. The results show that the volume prediction model of iron-making sintering end point is more accurate than that of the traditional model; compared with the traditional PID controller, the self-adaptive fuzzy controller based on volume prediction model not only performs better in speed and overshoot, but also has stronger robustness.
An adaptive fuzzy controller based on volume model for sintering end point position was designed based on the actual situation of 5# sintering machine in Meishan Iron and Steel Company of Baowu Group. Firstly,the least squares method was used to fit the exhaust temperature of the bellows on both sides of the sintering machine, and the volume prediction model of the sintering end point was established. The sintering end point position was determined by calculating the volume between the two curve vertices and the preset points. Secondly, an adaptive fuzzy controller was designed to realize self-adjustment of fuzzy rules by introducing weighting factors, which was verified by simulation in Matlab. The results show that the volume prediction model of iron-making sintering end point is more accurate than that of the traditional model; compared with the traditional PID controller, the self-adaptive fuzzy controller based on volume prediction model not only performs better in speed and overshoot, but also has stronger robustness.
引文
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[13]王春生,吴敏,曹卫华,等.铅锌烧结配料过程的智能集成建模与综合优化方法[J].自动化学报,2009,35(5):605-612.
[14]袁晓红,何花.烧结系统终点温度与透气性算法神经网络模型[J].计算机工程与设计,2006,27(6):1028-1029.
[2]周翠霞,胡文江,赵军,等.烧结终点的模糊控制[J].科技创新与应用,2016(6):54.
[3]丁园,王斌,鄢进冲,等.基于二维区间自回归模型的烧结终点预测[J].烧结球团,2017,42(3):1-7.
[4]荆锴,王旭明,李扬,等.基于ANFIS的烧结终点预测[J].烧结球团,2011,36(5):1-7.
[5]王介生.烧结过程软测量建模综述[J].烧结球团,2007,32(4):31-36.
[6]杨寅华.烧结过程的模糊控制系统设计[J].计算机应用与软件,2006,23(6):8-11.
[7]寇传乾.基于废气温度上升点的烧结终点预报系统[J].烧结球团,2007,32(5):1-6.
[8]李家新,胡林,龙红明.烧结终点控制优化策略的实现[J].安徽工业大学学报(自然科学版),2010,27(4):339-343.
[9]李明河,孙雁飞.烧结终点模糊控制应用探讨[J].安徽工业大学学报(自然科学版),2004,21(3):193-195.
[10]Khoo Suiyang.Sliding mode control for T-S fuzzy system[J].International Journal of Systems and Control,2007,2(1):21-29.
[11]SHANG X Q,LU J G,SUN Y X,et al.Data-driven prediction of sintering burn-through point based on novel genetic programming[J].Journal of Iron and Steel Research(International),2010,17(12):1-5.
[12]范晓慧,申炳听,陈许玲,等.烧结自动控制新技术(上)[J].烧结球团,2002,27(1):28-35.
[13]王春生,吴敏,曹卫华,等.铅锌烧结配料过程的智能集成建模与综合优化方法[J].自动化学报,2009,35(5):605-612.
[14]袁晓红,何花.烧结系统终点温度与透气性算法神经网络模型[J].计算机工程与设计,2006,27(6):1028-1029.