DocumentCode :
1338960
Title :
ANNNAC-extension of adaptive backstepping algorithm with artificial neural networks
Author :
Knohl, T. ; Unbehauen, H.
Author_Institution :
Control Eng. Lab., Ruhr-Univ., Bochum, Germany
Volume :
147
Issue :
2
fYear :
2000
fDate :
3/1/2000 12:00:00 AM
Firstpage :
177
Lastpage :
183
Abstract :
The adaptive backstepping algorithm is a well-known scheme for the design of nonlinear adaptive controllers. The two main drawbacks associated with this algorithm are that the nonlinear system must be linearly parameterised in the unknown or uncertain parameters and that the nonlinear functions must be exactly known. To avoid these problems, an extension of the backstepping algorithm with a specific type of artificial neural networks (ANN) called radial basis function networks (RBF), is proposed. This extension leads to a new control scheme: namely artificial neural network nonlinear adaptive control (ANNNAC). To further clarify the approach, a simple example is studied and the simulation results demonstrate clearly the power of this extension
Keywords :
Lyapunov methods; adaptive control; control system synthesis; feedback; matrix algebra; neurocontrollers; nonlinear control systems; parameter estimation; radial basis function networks; ANNNAC; adaptive backstepping algorithm; artificial neural network nonlinear adaptive control;
fLanguage :
English
Journal_Title :
Control Theory and Applications, IEE Proceedings -
Publisher :
iet
ISSN :
1350-2379
Type :
jour
DOI :
10.1049/ip-cta:20000193
Filename :
843255
Link To Document :
بازگشت