Direct RBF neural network control of a class of discrete-time non-affine nonlinear systems

Author

Zhang, J. ; Ge, S.S. ; Lee, T.H.

Author_Institution

Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore

Volume

1

fYear

2002

fDate

2002

Firstpage

424

Abstract

Direct adaptive RBF NN control is presented for a class of discrete-time single-input single-output non-affine nonlinear systems. An implicit function theorem is used to prove the existence and uniqueness of the implicit desired feedback control. Based on the input-output model, RBF neural networks are used to emulate the implicit desired feedback control. The closed-loop is proven to be semi-globally uniformly ultimately bounded if the design parameters are suitably chosen under certain mild conditions. Simulation results show the effectiveness of the direct RBF neural network control.

Keywords

adaptive control; closed loop systems; discrete time systems; feedback; neurocontrollers; nonlinear control systems; radial basis function networks; direct adaptive radial basis function neural network control; discrete-time single-input single-output nonaffine nonlinear systems; feedback control; implicit function theorem; input-output model; semi-globally uniformly ultimately bounded closed-loop; Adaptive control; Computer networks; Control systems; Feedback control; Neural networks; Neurons; Nonlinear control systems; Nonlinear systems; Physics computing; Programmable control;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 2002. Proceedings of the 2002

ISSN

0743-1619

Print_ISBN

0-7803-7298-0

Type

conf

DOI

10.1109/ACC.2002.1024842

Filename

1024842