http://iet.metastore.ingenta.com
1887

Adaptive control of discrete-time nonlinear systems using recurrent neural networks

Adaptive control of discrete-time nonlinear systems using recurrent neural networks

For access to this article, please select a purchase option:

Buy article PDF
$19.95
(plus tax if applicable)
Buy Knowledge Pack
10 articles for $120.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IEE Proceedings - Control Theory and Applications — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

A learning and adaptive control scheme for a general class of unknown MIMO discrete-time nonlinear systems using multilayered recurrent neural networks (MRNNs) is presented. A novel MRNN structure is proposed to approximate the unknown nonlinear input-output relationship, using a dynamic back propagation (DBP) learning algorithm. Based on the dynamic neural model, an extension of the concept of the input-output linearisation of discrete-time nonlinear systems is used to synthesise a control technique for model reference control purposes. A dynamic learning control architecture is developed with simultaneous online identification and control. The potentials of the proposed methods are demonstrated by simulation studies.

http://iet.metastore.ingenta.com/content/journals/10.1049/ip-cta_19949976
Loading

Related content

content/journals/10.1049/ip-cta_19949976
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address