基于二阶段自适应多模型的聚合釜温度控制

王振雷; 毛福兴; 王昕

doi:10.16511/j.cnki.qhdxxb.2016.24.023

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清华大学学报（自然科学版） ›› 2016, Vol. 56 ›› Issue (7) : 707-716. DOI: 10.16511/j.cnki.qhdxxb.2016.24.023

化学与化学工程

基于二阶段自适应多模型的聚合釜温度控制

王振雷¹, 毛福兴¹, 王昕²

作者信息 +

Temperature control of an acrylonitrile polymerization kettle using multiple models with second level adaptation

WANG Zhenlei¹, MAO Fuxing¹, WANG Xin²

Author information +

文章历史 +

摘要

针对丙烯腈聚合过程的强时滞和较大参数不确定等特性，该文提出一种基于二阶段自适应多模型的广义预测控制方法。该方法首先根据系统的参数范围，建立多个自适应模型，应用最小二乘算法分别进行参数估计。再利用各自适应模型的参数估计值和预报误差计算模型的权值，将各参数估计值加权求和得到最终参数估计值。将该参数估计值作为参数的真值，利用广义预测控制算法确定各时刻的控制作用。仿真结果显示：该方法能使系统未知参数快速收敛到真值，同时系统的动态性能和对理想温度的跟踪精度较常规多模型自适应控制有明显的提高。

Abstract

A generalized predictive control method was developed from multiple models with second level adaptation for temperature control the acrylonitrile polymerization process which has long time delays and large parameter uncertainties. Several adaptive models are designed for the system parameter ranges with the parameters estimated by a recursive least squares algorithm. Then, the model weights are calculated based on the parameter estimates and the prediction error of each model. Then, the parameter estimates are used as the true values of the parameters to determine the control action via the generalized predictive control algorithm. Simulation results show that this method enables a system with unknown parameters to quickly converge to the true value. The system performance of the system and the tracking accuracy of the ideal temperature are significantly improved compared with conventional multiple model adaptive control.

导出引用

王振雷, 毛福兴, 王昕. 基于二阶段自适应多模型的聚合釜温度控制[J]. 清华大学学报（自然科学版）. 2016, 56(7): 707-716 https://doi.org/10.16511/j.cnki.qhdxxb.2016.24.023

WANG Zhenlei, MAO Fuxing, WANG Xin. Temperature control of an acrylonitrile polymerization kettle using multiple models with second level adaptation[J]. Journal of Tsinghua University(Science and Technology). 2016, 56(7): 707-716 https://doi.org/10.16511/j.cnki.qhdxxb.2016.24.023

中图分类号： TP273

参考文献

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基金

国家自然科学基金面上基金资助项目（21376077）；国家自然科学基金优秀青年基金资助项目（61222303）；上海市自然科学基金资助项目（14ZR1410000，14ZR1421800）；上海市重点学科建设基金资助项目（B504）；流程工业综合自动化国家重点实验室开放课题基金资助项目（PALN201404）

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收稿日期	出版日期
2015-08-25	2016-07-15
发布日期
2016-07-15

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