磨矿分级过程控制知识在线提取与更新策略  

Data-driven control knowledge acquisition and online updating method for grinding classification process

作　　者：周佳怡 孙凯旋 王晓丽[1] 阳春华[1] 邹美吟 ZHOU Jia-yi;SUN Kai-xuan;WANG Xiao-liy;YANG Chun-hua;ZOU Mei-yin(School of Automation,Central South University,Changsha Hunan 410083,China)

机构地区：[1]中南大学自动化学院,湖南长沙410083

出　　处：《控制理论与应用》2025年第2期217-225,共9页Control Theory & Applications

基　　金：湖南省自然科学基金项目(2021JJ30872);国家自然科学基金项目(62073342);湖南省研究生科研创新项目(CX20210240)资助.

摘　　要：磨矿过程机理复杂、流程长,传统的优化控制方法自我学习能力薄弱,难以在复杂矿源和工况条件的频繁变化下长期有效地做出控制决策.因此,本文提出一种磨矿分级过程控制知识在线提取及更新策略.首先,基于状态转移的核模糊C均值聚类算法对磨矿分级过程运行工况进行精细划分,结合过程运行特性及聚类结果确定最优工况.然后,基于加权优化的Wang Mendel算法提取不同工况的优化控制知识,并定义规则置信度进行知识评价.最后,基于双滑动窗口机制实现控制规则的在线更新.结果表明,相较于离线规则、自适应模糊控制和人工控制决策,在线控制规则具有更好的自适应能力.The mechanism of grinding process is complex and the flow is long.The traditional optimal control method has weak self-learning ability,and it is difficult to make effective control response in long-time under the frequent changes of complex ore sources and operating conditions.Therefore,a strategy for online extracting and updating the control knowledge of grinding-classification process is proposed in this paper.Firstly,the operating conditions of grinding classification process are divided finely based on state transition algorithm-based kernel fuzzy C-means clustering method.The optimal operating conditions are then selected by combining the process operating characteristics and clustering results.Next,a Wang Mendel algorithm based on weighted optimization is proposed to extract the optimal control knowledge of different operating conditions and rule confidence is defined to evaluate the effectiveness of the knowledge.Finally,the control rules are updated online based on the double sliding window mechanism.The results show that compared with control using offline rules,adaptive fuzzy logic control and manual control,control using online rules has better adaptive ability.

关 键 词：磨矿分级过程 优化控制 知识提取 在线更新 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]