Neuro-adaptive tracking control algorithms for a class of nonlinear systems

Author

Song, Y.D.

Author_Institution

Dept. of Electr. Eng., North Carolina A&T State Univ., Greensboro, NC, USA

Volume

1

fYear

1997

fDate

4-6 Jun 1997

Firstpage

664

Abstract

Presents a neural network (NN) based adaptive control method for a class of nonlinear dynamic systems. Two NN units are incorporated into control scheme which are shown to be effective in attenuating NN reconstruction error and other lumped system uncertainties. Since the control scheme is based upon the worst case that the NNs might behave, it exhibits a “fail-safe” feature, which enhances the reliability of the NN-based control scheme. Stable online weights tuning algorithms are derived based on Lyapunov stability theory. The control method is extended to robotic systems

Keywords

Lyapunov methods; adaptive control; neurocontrollers; nonlinear dynamical systems; position control; reliability; robot dynamics; stability; tuning; Lyapunov stability theory; fail-safe feature; lumped system uncertainties; neuro-adaptive tracking control algorithms; nonlinear dynamic systems; online weights tuning algorithms; reconstruction error; reliability; robotic systems; Automatic control; Control systems; Error correction; Matrix decomposition; Neural networks; Nonlinear control systems; Nonlinear dynamical systems; Nonlinear systems; Uncertainty; Upper bound;

fLanguage

English

Publisher

ieee

Conference_Titel

American Control Conference, 1997. Proceedings of the 1997

Conference_Location

Albuquerque, NM

ISSN

0743-1619

Print_ISBN

0-7803-3832-4

Type

conf

DOI

10.1109/ACC.1997.611884

Filename

611884