A Model of Feedback Error Learning Based on Kalman Estimator

Author

Ruan, Xiaogang ; Liu, Liang ; Yu, Naigong ; Ding, Mingxiao

Author_Institution

Dept. of Electron. Inf. & Control Eng., Beijing Univ. of Technol.

Volume

1

fYear

0

fDate

0-0 0

Firstpage

4190

Lastpage

4194

Abstract

Based on the feedback error learning (FEL) method and Michael et al.´s research work on dynamical state estimation, a new model of motor control system is proposed to overcome the drawback deriving from time delay. In the new model, the supervised signal derives from both the output of the Kalman estimator and the feedback motor command, and this comprehensive signal provides the instructive information to train the forward neural network in the cerebellar cortex. The effectiveness of the proposed new model is demonstrated by simulation experiments on inverted pendulum

Keywords

feedback; feedforward neural nets; nonlinear systems; state estimation; Kalman estimator; cerebellar cortex; dynamical state estimation; feedback error learning; feedback motor command; forward neural network; inverted pendulum; motor control system; time delay; Brain modeling; Delay effects; Error correction; Kalman filters; Motor drives; Neural networks; Neurofeedback; Output feedback; State estimation; State feedback; Kalman filter; cerebellum; feedback error learning; internal mode; state estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Control and Automation, 2006. WCICA 2006. The Sixth World Congress on

Conference_Location

Dalian

Print_ISBN

1-4244-0332-4

Type

conf

DOI

10.1109/WCICA.2006.1713164

Filename

1713164