NEURAL NETWORK PREDICTIVE CONTROL OF A CHEMICAL REACTOR

被引：0

作者：

Vasickaninova, A. ^{[1
]}

Bakosova, M. ^{[1
]}

机构：

[1] Slovak Univ Technol Bratislava, Inst Informat Engn Automat & Math, Fac Chem & Food Technol, Dept Informat Engn & Proc Control, Bratislava 81237, Slovakia

来源：

23RD EUROPEAN CONFERENCE ON MODELLING AND SIMULATION (ECMS 2009) | 2009年

关键词：

Model predictive control; neural network; continuous stirred tank reactor; CONTROL-SYSTEMS; MODEL;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Model Predictive Control (MPC) refers to a class of algorithms that compute a sequence of manipulated variable adjustments in order to optimize the future behaviour of a plant. MPC technology can now be found in a wide variety of application areas. The neural network predictive controller that is discussed in this paper uses a neural network model of a nonlinear plant to predict future plant performance. The controller calculates the control input that will optimize plant performance over a specified future time horizon. In the paper, simulation of the neural network based predictive control for the continuous stirred tank reactor is presented. The simulation results are compared with fuzzy and PID control.

引用

页码：563 / 569

页数：7

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