基于子空间模型的煮糖结晶过程入料流量自适应控制  

Adaptive control of syrup flow based on subspace identification model in cane sugar crystallization process

作　　者：韦锦[1] 蓝启亮 蒙艳玫[1] 李广全 张金来 陈剑[1] WEI Jin;LAN Qi-liang;MENG Yan-mei;LI Guang-quan;ZHANG Jin-lai;CHEN Jian(College of Mechanical Engineering,Guangxi University,Nanning 530004,Chin)

机构地区：[1]广西大学机械工程学院,广西南宁530004

出　　处：《广西大学学报（自然科学版）》2018年第5期1786-1793,共8页Journal of Guangxi University（Natural Science Edition）

基　　金：国家自然科学基金资助项目(51465003)

摘　　要：为研究具有非线性大时滞特点的煮糖过程入料流量的自适应控制问题,从系统辨识的角度入手,通过子空间辨识法建立入料流量与电动阀门开度之间的预测模型,利用改进型最小二乘法构建目标函数简化运算,通过滚动优化来整定模型的输出误差以实现对入料流量的自适应控制。仿真结果表明,子空间预测模型的训练时间为0. 888 3 s,平均绝对误差为0. 002 6,明显优于BP神经网络预测模型和RBF神经网络预测模型的预测性能,符合煮糖过程工艺控制的要求。最后通过自主研发的煮糖过程综合实验平台对该控制算法的有效性和优越性进行了实验验证。In order to study the self-adaptive control of the feed syrup flow during the sugar crystallization process with nonlinear and large time delay,based on the perspective of system identification,the prediction model between the inlet syrup flow and the electric valve opening was established by the subspace identification method,and the improved least square method was used to construct the target.The function simplifies the calculation and adjusts the output error of the model through rolling optimization to achieve adaptive control of the incoming flow.The simulation analysis results show that the training time of the subspace prediction model is 0.888 3 s,and the average absolute error is 0.002 6,which is obviously better than those of BP neural network prediction model and RBF neural network prediction,and is in line with the process control requirements of the sugar crystallization process.Finally,the experiment was carried out through the self-developed comprehensive experiment platform for crystallization process which demonstrate the effectiveness and superiority of the control algorithm.

关 键 词：煮糖过程 子空间辨识 预测模型控制 自适应控制 入料流量 

分 类 号：TP29[自动化与计算机技术—检测技术与自动化装置]