Generalized stochastic state space neural adaptive control

Author

Ho, Tuan T.

Author_Institution

Adv. Syst. Res., Aurora, CO, USA

fYear

1993

fDate

25-27 Aug 1993

Firstpage

133

Lastpage

138

Abstract

A generalized stochastic neural adaptive control algorithm is presented, where the system identification is based on the state space innovations model and a neural network architecture. This identification algorithm is derived from three different optimization approaches, i.e., the gradient, Newton, and minimum variance. The control law, also based on neural networks structure, is derived from a quadratic (one-step-ahead prediction) performance index, which in combination with neural identification constitutes a unique neural adaptive control algorithm with excellent performance

Keywords

adaptive control; identification; neurocontrollers; optimisation; performance index; state-space methods; stochastic systems; Newton method; generalized stochastic neural adaptive control algorithm; gradient method; minimum variance; neural identification; neural network architecture; optimization; quadratic performance index; state space innovations model; system identification; Adaptive control; Neural networks; Performance analysis; Predictive models; State estimation; State-space methods; Stochastic processes; Stochastic systems; System identification; Technological innovation;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control, 1993., Proceedings of the 1993 IEEE International Symposium on

Conference_Location

Chicago, IL

ISSN

2158-9860

Print_ISBN

0-7803-1206-6

Type

conf

DOI

10.1109/ISIC.1993.397644

Filename

397644