Recurrent RBFN-based fuzzy neural network control for X-Y-/spl Theta/ motion control stage using linear ultrasonic motors

A recurrent radial basis function network (RBFN) based fuzzy neural network (FNN) control system is proposed to control the position of an X-Y-Theta motion control stage using linear ultrasonic motors (LUSMs) to track various contours in this study. The proposed recurrent RBFN-based FNN combines the merits of self-constructing fuzzy neural network (SCFNN), recurrent neural network (RNN), and RBFN. Moreover, the structure and the parameter learning phases of the recurrent RBFN-based FNN are performed concurrently and on line. The structure learning is based on the partition of input space, and the parameter learning is based on the supervised gradient decent method using a delta adaptation law. The experimental results due to various contours show that the dynamic behaviors of the proposed recurrent RBFN-based FNN control system are robust with regard to uncertainties

[1]  David L. Elliott,et al.  Neural Systems for Control , 1997 .

[2]  Chin-Teng Lin,et al.  An online self-constructing neural fuzzy inference network and its applications , 1998, IEEE Trans. Fuzzy Syst..

[3]  Hassan K. Khalil,et al.  Output feedback control of nonlinear systems using RBF neural networks , 2000, IEEE Trans. Neural Networks Learn. Syst..

[4]  Chia-Hsiang Menq,et al.  Large travel ultra precision x-y-/spl theta/ motion control of a magnetic-suspension stage , 2003 .

[5]  Li-Xin Wang,et al.  A Course In Fuzzy Systems and Control , 1996 .

[6]  Les A. Piegl,et al.  The NURBS Book , 1995, Monographs in Visual Communication.

[7]  Derong Liu,et al.  Neural network-based model reference adaptive control system , 2000, IEEE Trans. Syst. Man Cybern. Part B.

[8]  Toshiiku Sashida,et al.  An Introduction to Ultrasonic Motors , 1994 .

[9]  Chuen-Tsai Sun,et al.  Functional equivalence between radial basis function networks and fuzzy inference systems , 1993, IEEE Trans. Neural Networks.

[10]  Faa-Jeng Lin,et al.  Adaptive hybrid control using a recurrent neural network for a linear synchronous motor servo-drive system , 2001 .

[11]  Bhaskar D. Rao,et al.  On-line learning algorithms for locally recurrent neural networks , 1999, IEEE Trans. Neural Networks.

[12]  Faa-Jeng Lin,et al.  Self-constructing recurrent fuzzy neural network for DSP-based permanent-magnet linear-synchronous-motor servodrive , 2006 .

[13]  Meenakshisundaram Gopi,et al.  A Unified Architecture for the Computation of B-Spline Curves and Surfaces , 1997, IEEE Trans. Parallel Distributed Syst..

[14]  Faa-Jeng Lin,et al.  A permanent-magnet synchronous motor servo drive using self-constructing fuzzy neural network controller , 2004, IEEE Transactions on Energy Conversion.

[15]  Rong-Jong Wai,et al.  Recurrent fuzzy neural network control for piezoelectric ceramic linear ultrasonic motor drive. , 2001, IEEE transactions on ultrasonics, ferroelectrics, and frequency control.

[16]  Chin-Teng Lin,et al.  An On-Line Self-Constructing Neural Fuzzy Inference Network and Its Applications , 1998 .

[17]  E. Mizutani,et al.  Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review] , 1997, IEEE Transactions on Automatic Control.

[18]  Nesbitt W. Hagood,et al.  Modeling of a piezoelectric rotary ultrasonic motor , 1994, IEEE Transactions on Ultrasonics, Ferroelectrics and Frequency Control.

[19]  Rong-Jong Wai,et al.  A supervisory fuzzy neural network control system for tracking periodic inputs , 1999, IEEE Trans. Fuzzy Syst..

[20]  Rong-Jong Wai,et al.  Ultrasonic motor servo-drive with online trained neural-network model-following controller , 1998 .