基于矩生成网络的草酸钴工艺粒度分布迭代控制

Lv Chao, Wang Jing, Jin Qibing, Zhou Jinglin, Wu HaiYan
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引用次数: 0

摘要

由于缺乏有效的颗粒粒径分布测量方法,颗粒粒径分布的控制非常复杂。为了解决这一问题,提出了一种基于矩量生成网络模型的迭代学习控制策略。将无法在线测量的PSD跟踪控制转化为可测力矩控制。制定控制策略有两个步骤。首先,建立力矩生成网络,建立力矩与过程测量之间的关系;然后根据过程的重复性设计迭代学习控制策略,驱动过程达到目标PSD。以草酸钴合成工艺为例,对控制策略的性能进行了测试。实验结果表明,该方法具有较强的收敛能力和抗干扰能力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Iteration control of particle size distribution in cobalt oxalate process based on moment generation network
Particle size distribution (PSD) control is very complicated to realize due to the absence of effective measuring methods for PSD. In order to solve this problem, an iteration learning control strategy based on the moment generation network model is proposed. The tracking control of PSD which cannot be measured online is converted into the measurable moment control. There are two steps to develop the control strategy. Firstly, a moment generation network is built to construct the relationship between the moment and process measurements. Then an iteration learning control strategy is designed to drive the process to achieve a target PSD according to the repetitive nature of the process. The cobalt oxalate synthesis process was selected to test the performance of the control strategy. The experimental results demonstrated that the approach had a strong ability in convergence and resisting disturbance.
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