Neural-network-based composite disturbance rejection control for a distillation column

Author:

Li Juan12,Li Shihua1,Li Shengquan2,Chen Xisong1,Yang Jun1

Affiliation:

1. School of Automation, Southeast University, Nanjing, P. R. China

2. School of Hydraulic, Energy and Power Engineering, Yangzhou University, Yangzhou, P. R. China

Abstract

Binary distillation columns are essentially multi-variable systems with couplings, non-minimum phase characteristics, model mismatches and various external disturbances. To get the desired top (distillate) and bottom product composition, a composite disturbance rejection control strategy using a radial basis function network (RBFN) is proposed in this paper. The composite controller includes neural network inverse controller (NNIC) and neural network disturbance observer (NNDOB) both using the inverse model of system which is identified by the RBFN. The stability of the identified inverse model is proved, and a rigorous analysis is also given to show why the NNDOB can effectively suppress the disturbances. Performances of the proposed scheme are compared with PID and NNIC without disturbance compensation in three cases by simulation studies. The simulations demonstrate the feasibility, effectiveness and disturbance rejection property of the proposed method in controlling the product composition of the binary distillation columns.

Publisher

SAGE Publications

Subject

Instrumentation

Cited by 5 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3