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

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.

[1]  Chun-Shin Lin,et al.  Learning convergence of CMAC technique , 1997, IEEE Trans. Neural Networks.

[2]  Mietek A. Brdys,et al.  Dynamic neural controllers for induction motor , 1999, IEEE Trans. Neural Networks.

[3]  Chuanyi Ji,et al.  Fast training of recurrent networks based on the EM algorithm , 1998, IEEE Trans. Neural Networks.

[4]  Z. Jason Geng,et al.  Missile Control Using Fuzzy Cerebellar Model Arithmetic Computer Neural Networks , 1997 .

[5]  P. C. Parks,et al.  Convergence Properties of Associative Memory Storage for Learning Control Systems , 1990 .

[6]  Lida Xu,et al.  A new type of recurrent fuzzy neural network for modeling dynamic systems , 2001, Knowl. Based Syst..

[7]  Andrew A. Goldenberg,et al.  Neural-network control of mobile manipulators , 2001, IEEE Trans. Neural Networks.

[8]  James S. Albus,et al.  New Approach to Manipulator Control: The Cerebellar Model Articulation Controller (CMAC)1 , 1975 .

[9]  Sheng-De Wang,et al.  Adaptive tuning of the fuzzy controller for robots , 2000, Fuzzy Sets Syst..

[10]  Nabil Derbel,et al.  Fuzzy control of robot manipulators , 2002 .

[11]  Guanrong Chen,et al.  A fuzzy adaptive variable structure controller with applications to robot manipulators , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[12]  A. Sideris,et al.  Learning convergence in the cerebellar model articulation controller , 1992, IEEE Trans. Neural Networks.

[13]  Rong-Jong Wai,et al.  Hybrid supervisory control using recurrent fuzzy neural network for tracking periodic inputs , 2001, IEEE Trans. Neural Networks.

[14]  W. T. Miller,et al.  CMAC: an associative neural network alternative to backpropagation , 1990, Proc. IEEE.

[15]  Jun Wang,et al.  A recurrent neural network for nonlinear optimization with a continuously differentiable objective function and bound constraints , 2000, IEEE Trans. Neural Networks Learn. Syst..

[16]  James S. Albus,et al.  I A New Approach to Manipulator Control: The I Cerebellar Model Articulation Controller , 1975 .