ANFIS model-based predictive control for Pr/Nd cascade extraction process

YANG Hui; ZHU Fan; LU Rongxiu; ZHANG Zhiyong

doi:10.11949/j.issn.0438-1157.20151978

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ANFIS model-based predictive control for Pr/Nd cascade extraction process

更新时间：2025-12-10

- ANFIS model-based predictive control for Pr/Nd cascade extraction process
- CIESC Journal Issue 3, Pages: 982-990(2016)
- 作者机构：
  
  华东交通大学电气与电子工程学院,江西,南昌,330013
- 作者简介：
- 基金信息：
- DOI：10.11949/j.issn.0438-1157.20151978
  CLC： Q028.8
- Online First：12 January 2016，
  
  Published：12 January 2016
- 稿件说明：
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YANG Hui, ZHU Fan, LU Rongxiu, et al. ANFIS model-based predictive control for Pr/Nd cascade extraction process[J]. CIESC Journal, 2016, (3): 982-990.
DOI：

YANG Hui, ZHU Fan, LU Rongxiu, et al. ANFIS model-based predictive control for Pr/Nd cascade extraction process[J]. CIESC Journal, 2016, (3): 982-990. DOI： 10.11949/j.issn.0438-1157.20151978.

摘要

针对稀土萃取过程自动化程度低、经验控制误差大、手动调节效率不高的问题

建立了萃取过程ANFIS模型

实现了各控制流量的自动调节。考虑稀土萃取过程非线性和动态特性

采用自适应神经模糊推理系统(ANFIS)对Pr/Nd萃取过程进行描述

在保证高精度的组分含量预测输出基础上

运用广义预测控制方法(GPC)实现各控制流量的优化控制;最后

基于Pr/Nd萃取过程动态数据进行仿真实验。通过与传统PID方法的实验对比

表明了本文方法的有效性。

Abstract

Rare earth (RE) is a national major strategic resource

but there are some problems existed in the RE cascade extraction industry

such as poor levels of automation

large control error and low efficiency of manual adjustment. In this paper a non-linear generalized predictive control (GPC) method based on adaptive neural fuzzy inference system (ANFIS) is proposed to counter these problems. First

in consideration of the nonlinearity and dynamic characteristic of the extraction process

the ANFIS algorithm is employed to describe the process. Then

on the premise of high-precision of component content prediction

the GPC method is exploited to adjust the flows accurately and automatically. Finally

simulation experiments are carried out based on the dynamic data of Pr/Nd cascade extraction process. By the contrast with the conventional PID method

it is validated that the proposed approach is effective.

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