NEURAL MODEL-PREDICTIVE CONTROL FOR NONLINEAR CHEMICAL PROCESSES

被引：35

作者：

SONG, JJ ^{[1
]}

PARK, S ^{[1
]}

机构：

[1] KOREA ADV INST SCI & TECHNOL,DEPT CHEM ENGN,PROC SYST LAB,373-1 KUSONG DONG,YUSUNG GU,TAEJON 305701,SOUTH KOREA

来源：

JOURNAL OF CHEMICAL ENGINEERING OF JAPAN | 1993年 / 26卷 / 04期

关键词：

NEURAL NETWORK; MODEL PREDICTIVE CONTROL; IDENTIFICATION; INTELLIGENT CONTROL; NONLINEAR CONTROL;

D O I：

10.1252/jcej.26.347

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

A neural model predictive control strategy combining a neural network for plant identification and a nonlinear programming algorithm for solving nonlinear control problems is proposed. A constrained nonlinear optimization approach using successive quadratic programming combined with a neural identification network is used to generate the optimum control law for complex continuous chemical reactor systems that have inherent nonlinear dynamics. The neural model predictive controller (NMPC) shows good performance and robustness.

引用

页码：347 / 354

页数：8

共 28 条

[1]

[Anonymous], 1987, LEARNING INTERNAL RE

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