Multivariable predictive neuronal control applied to grinding plants

Author

Duarte, M. Manuel ; Suárez, S. Alejandro ; Bassi, Danilo

Author_Institution

Chile Univ., Santiago, Chile

Volume

2

fYear

1999

fDate

1999

Firstpage

975

Abstract

This work investigates the use of a direct neural network predictive controller applied to a grinding plant. A phenomenological model of the grinding plant is used to simulate the control strategies. The model is based on a mass balance and power consumption of the mill containing 32 particle size intervals. The controller neural network is trained by using an estimation of the error. Several tests are performed driving the nonlinear process to an operation point and then controlling it by training the net online, which enables monitoring of the range over which the neural controller is still valid, without having to conceive a linear model of the process

Keywords

grinding; learning (artificial intelligence); multivariable control systems; neurocontrollers; nonlinear control systems; predictive control; process control; process monitoring; controller neural network; error estimation; grinding plants; mass balance; monitoring; multivariable predictive neuronal control; nonlinear process; online training; phenomenological model; power consumption; Artificial neural networks; Circuits; Control systems; Error correction; Feeds; Milling machines; Neural networks; Predictive control; Predictive models; Three-term control;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Processing and Manufacturing of Materials, 1999. IPMM '99. Proceedings of the Second International Conference on

Conference_Location

Honolulu, HI

Print_ISBN

0-7803-5489-3

Type

conf

DOI

10.1109/IPMM.1999.791514

Filename

791514