Disturbance rejection using error estimation in neural network controller design

Author

Chan, Patrick P K ; Peng, Bo ; Ng, Wing W Y ; Yeung, Daniel S.

Author_Institution

Machine Learning & Cybern. Res. Center, South China Univ. of Technol., Guangzhou, China

Volume

3

fYear

2011

fDate

10-13 July 2011

Firstpage

1220

Lastpage

1225

Abstract

Disturbance rejection is an important factor in evaluating the performance of a control system. By using error estimations, we expand a virtual area among actual error points in the error space which is composed of runtime errors and their derivatives. Rather than driving our neural network controller (NNC) with actual error signals, we utilize virtual error signals under different expanding parameters. Simulations have successfully shown that out method could resist unexpected disturbance in many cases.

Keywords

control system synthesis; estimation theory; neurocontrollers; control system; disturbance rejection; error estimation; neural network controller design; performance evaluation; virtual error signals; Artificial neural networks; Chaos; Control systems; Cybernetics; Error analysis; Machine learning; Disturbance rejection; Error estimations; Intelligent control; Neural network controller; Sensitivity;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2011 International Conference on

Conference_Location

Guilin

ISSN

2160-133X

Print_ISBN

978-1-4577-0305-8

Type

conf

DOI

10.1109/ICMLC.2011.6016931

Filename

6016931