DocumentCode :
1369821
Title :
Neural controllers for nonlinear state feedback L2-gain control
Author :
Ahmed, M.S.
Author_Institution :
DaimlerChrysler Corp., Auburn Hill, MI, USA
Volume :
147
Issue :
3
fYear :
2000
fDate :
5/1/2000 12:00:00 AM
Firstpage :
239
Lastpage :
246
Abstract :
Design of an L2-gain disturbance rejection neural controller for nonlinear systems is presented. The control input is generated from a radial basis network, which is trained offline such that a computed partial derivative of the network output satisfies a Hamilton-Jacobi inequality. Once the network is successfully trained for a given manifold in the state space, the closed-loop system ensures a finite gain between the system disturbance and the system input-output as long as the system states remain within the state manifold. The proposed method may also be applied to obtain an H controller
Keywords :
closed loop systems; control system synthesis; neurocontrollers; nonlinear control systems; radial basis function networks; state feedback; H controller; Hamilton-Jacobi inequality; disturbance rejection neural controller; nonlinear state feedback L2-gain control; radial basis network;
fLanguage :
English
Journal_Title :
Control Theory and Applications, IEE Proceedings -
Publisher :
iet
ISSN :
1350-2379
Type :
jour
DOI :
10.1049/ip-cta:20000342
Filename :
859022
Link To Document :
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