Neural Network-Based PID Gain Tuning of Chemical Plant Controller

被引：1

作者：

Abe, Yoshihiro ^{[1
]}

Konishi, Masami ^{[1
]}

Imai, Jun ^{[1
]}

Hasegawa, Ryusaku ^{[2
]}

Watanabe, Masanori ^{[2
]}

Kamijo, Hiroaki ^{[2
]}

机构：

[1] Okayama Univ, Okayama 7008530, Japan

[2] Nippon Petr Refinery Co, Tokyo, Japan

来源：

ELECTRICAL ENGINEERING IN JAPAN | 2010年 / 171卷 / 04期

关键词：

matrix converter; reactive power compensation; active filter;

D O I：

10.1002/eej.20973

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Plant control systems are now highly automated and are used in many industries. The control performance changes with the passage of time because of deterioration of plant facilities. For this reason, human experts tune the control system to improve overall plant performance. In this study, a PID control system for the oil refining chemical plant process is discussed. In oil refining, thousands of control loops are used in plants in order to keep the product quality at the desired value and to assure the safety of plant operation. Due to the ambiguity of the interference between control loops, it is difficult to estimate the plant dynamical model accurately. Using a neuro emulator and a recurrent neural networks model (RNN model) for emulation and tuning of parameters, a PID gain tuning system for a chemical plant controller is constructed. Numerical experiments using actual plant data demonstrate the effect of the proposed method. (C) 2010 Wiley Periodicals, Inc. Electr Eng Jpn, 171(4): 28-36, 2010; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/eej.20973

引用

页码：28 / 36

页数：9

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