Trajectory-Tracking Control for Mobile Robot Using Recurrent Fuzzy Cerebellar Model Articulation Controller

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 parameters. Simulation experiments are made by applying proposed RFCMAC on mobile robots tracking control problem to confirm its effectiveness, and has better dynamic performance than FCMAC.

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

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

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

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

[5]  Ching-Hung Lee,et al.  Tracking control of mobile robots using saturation feedback controller , 1999, Proceedings 1999 IEEE International Conference on Robotics and Automation (Cat. No.99CH36288C).

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

[7]  Frank L. Lewis,et al.  Control of a nonholonomic mobile robot using neural networks , 1998, IEEE Trans. Neural Networks.

[8]  Sahin Yildirim.  Adaptive robust neural controller for robots , 2004, Robotics Auton. Syst..

[9]  Toshio Fukuda,et al.  Mobile robot control using fuzzy-Gaussian neural networks , 1993, Proceedings of 1993 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS '93).

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

[11]  Jong-Hwan Kim,et al.  Sliding mode control for trajectory tracking of nonholonomic wheeled mobile robots , 1999, IEEE Trans. Robotics Autom..

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

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

[14]  Farzad Pourboghrat,et al.  Adaptive control of dynamic mobile robots with nonholonomic constraints , 2002, Comput. Electr. Eng..

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

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

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