DocumentCode
390859
Title
Neural network-based sliding mode control for a class of uncertain systems with measurement noise
Author
Jinyong, Yang ; Yingmin, Jia
Author_Institution
Seventh Res. Div., Beijing Univ. of Aeronaut. & Astronaut., China
Volume
3
fYear
2002
fDate
28-31 Oct. 2002
Firstpage
1479
Abstract
In this paper, we consider sliding mode control (SMC) of uncertain systems whose output is contaminated by external disturbances. The cone-bounded assumption on uncertainties is removed via neural networks. The proposed sliding-mode controller can not only guarantee a uniform ultimate boundedness of states of the plant, but also the boundedness of all other signals in the closed-loop system.
Keywords
closed loop systems; neural nets; nonlinear systems; uncertain systems; variable structure systems; closed-loop system; measurement noise; neural network-based sliding mode control; nonlinear systems; uncertain systems; Adaptive control; Control systems; Extraterrestrial measurements; Measurement uncertainty; Neural networks; Noise measurement; Pollution measurement; Programmable control; Sliding mode control; Uncertain systems;
fLanguage
English
Publisher
ieee
Conference_Titel
TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering
Print_ISBN
0-7803-7490-8
Type
conf
DOI
10.1109/TENCON.2002.1182608
Filename
1182608
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