Recurrent Fuzzy Cerebellar Model Articulation Controller and Its Application on Robotic Tracking Control

Author

Peng, Jinzhu ; Wang, Yaonan ; Zhang, Hui

Author_Institution

Sch. of Electr. Eng., Zhengzhou Univ., Zhengzhou, China

Volume

2

fYear

2009

fDate

19-21 May 2009

Firstpage

293

Lastpage

297

Abstract

A kind of recurrent fuzzy cerebellar model articulation controller (RFCMAC) model is presented. The recurrent network is embedded in the RFCMAC by adding feedback connections on the first layer to embed temporal relations in the network. A nonconstant differentiable Gaussian basis function is used to model the hypercube structure and the fuzzy weight. A gradient descent learning algorithm is used to adjust the free parameters. Simulation experiments are made by applying proposed RFCMAC on robotic manipulator tracking control problem to confirm its effectiveness.

Keywords

Gaussian processes; backpropagation; cerebellar model arithmetic computers; feedback; fuzzy control; gradient methods; neurocontrollers; recurrent neural nets; robots; tracking; backpropagation; feedback; fuzzy weight; gradient descent learning algorithm; hypercube structure; nonconstant differentiable Gaussian basis function; recurrent fuzzy cerebellar model articulation controller; robotic tracking control; Feedforward neural networks; Feeds; Function approximation; Fuzzy control; Fuzzy neural networks; Intelligent robots; Neural networks; Recurrent neural networks; Robot control; Sliding mode control; cerebellar model articulation controller; fuzzy neural network; recurrent neural network; tracking control;

fLanguage

English

Publisher

ieee

Conference_Titel

Intelligent Systems, 2009. GCIS '09. WRI Global Congress on

Conference_Location

Xiamen

Print_ISBN

978-0-7695-3571-5

Type

conf

DOI

10.1109/GCIS.2009.184

Filename

5209428