Application of adaptive neural model-based control

Author

Mills, Peter M. ; Zomaya, Albert Y. ; Tadé, Moses O.

Author_Institution

CRA Adv. Tech. Dev., Cannington, WA, Australia

fYear

1993

fDate

15-17 Dec 1993

Firstpage

2804

Abstract

Previous work extending identification using neural networks to the online adaptive case has resulted in poor performance. The work presented in this paper demonstrates a powerful method for implementing an adaptive neural network model of nonlinear process dynamics for control. This adaptive method has been amalgamated with a multistep nonlinear predictive control technique. The performance of this controller is demonstrated, and evaluated, using two simulated realistic processes

Keywords

adaptive control; chemical industry; feedforward neural nets; identification; multivariable control systems; nonlinear control systems; predictive control; process control; transfer functions; adaptive multivariable control; adaptive neural identification; adaptive neural model-based control; chemical industry; evaporator process control; history stack adaptation; multistep nonlinear predictive control; nonlinear process dynamics; transfer function; Adaptive control; Adaptive systems; Australia; Convergence; Industrial control; Milling machines; Neural networks; Predictive control; Predictive models; Programmable control;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1993., Proceedings of the 32nd IEEE Conference on

Conference_Location

San Antonio, TX

Print_ISBN

0-7803-1298-8

Type

conf

DOI

10.1109/CDC.1993.325706

Filename

325706