Recurrent wavelet-based Elman neural network control for multi-axis motion control stage using linear ultrasonic motors

Author(s): F.-J. Lin ; Y.-S. Kung ; S.-Y. Chen ; Y.-H. Liu
DOI: 10.1049/iet-epa.2009.0144

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Author(s): F.-J. Lin ¹ ; Y.-S. Kung ² ; S.-Y. Chen ¹ ; Y.-H. Liu ¹
- Affiliations: 1: Department of Electrical Engineering, National Central University, Chungli, Taiwan
  2: Department of Electrical Engineering, Southern Taiwan University, Tainan, Taiwan
Source: Volume 4, Issue 5, May 2010, p. 314 – 332
DOI: 10.1049/iet-epa.2009.0144 , Print ISSN 1751-8660, Online ISSN 1751-8679

A novel recurrent wavelet-based Elman neural network (RWENN) control system is proposed in this study to control the mover position of a multi-axis motion control stage using linear ultrasonic motors (LUSMs) for the tracking of various contours. First, the structure and operating principles of the LUSMs are introduced briefly. Since the dynamic characteristics and motor parameters of the LUSMs are non-linear and time varying, the RWENN is proposed to control the mover of the X–Y–thetas motion control stage to track various contours precisely using a direct decentralised control strategy. In the proposed RWENN, each hidden neuron employs a different wavelet function as an activation function. Moreover, the recurrent connective weights are added in the RWENN. Therefore compared with the conventional Elman neural network (ENN), both the precision and time of convergence are improved. Furthermore, the on-line learning algorithm based on the supervised gradient descent method and the convergence analysis of the tracking error using a discrete-type Lyapunov function of the RWENN are developed. Finally, some experimental results of various contours tracking show that the tracking performance of the RWENN is significantly improved compared with the ENN.

References

1. 1)
  - B. Delyon , A. Juditsky , A. Benveniste . Accuracy analysis for wavelet approximations. IEEE Trans. Neural Netw. , 2 , 332 - 348
2. 2)
  - Y.C. Chen , C.C. Teng . A model reference control structure using a fuzzy neural network. Fuzzy Sets Syst. , 291 - 312
3. 3)
  - S. Jung , H.T. Cho , T.C. Hsia . Neural network control for position tracking of a two-axis inverted pendulum system experimental studies. IEEE Trans. Neural Netw. , 4 , 1042 - 1048
4. 4)
  - D. Sun , X. Shao , G. Feng . A model-free cross-coupled control for position synchronization of multi-axis motions: theory and experiments. IEEE Trans. Control Syst. Technol. , 306 - 314
5. 5)
  - T. Hu , W.J. Kim . Extended range six-DOF high-precision positioner for wafer processing. IEEE/ASME Trans. Mechatronics , 6 , 682 - 689
6. 6)
  - X. Li , G. Chen , Z. Chen , Z. Yuan . Chaotifying linear Elman networks. IEEE Trans. Neural Netw. , 5 , 1193 - 1199
7. 7)
  - R.C. Luo , J.H. Tzou . Implementation of a new adaptive slicing algorithm for the rapid prototyping manufacturing system. IEEE/ASME Trans. Mechatronics , 3 , 593 - 600
8. 8)
  - L. Ma , K. Khorasani . Constructive feedforward neural networks using Hermite polynomial activation functions. IEEE Trans. Neural Netw. , 821 - 833
9. 9)
  - Liu, H., Wang, S., Ouyang, P.: `Fault diagnosis based on improved Elman neural network for a hydraulic servo system', Int. Conf. on Robotics, Automation and Mechatronics, 2006, p. 1–6.
10. 10)
  - F.J. Lin , R.J. Wai , C.C. Lee . Fuzzy neural network position controller for ultrasonic motor drive using push-pull DC–DC converter. IET Proc., Control Theory Appl. , 1 , 99 - 107
11. 11)
  - D.T. Pham , X. Liu . Training of Elman networks and dynamic system modelling. Int. J. Syst. Sci. , 2 , 221 - 226
12. 12)
  - O. Omidvar , D.L. Elliott . (1997) Neural systems for control.
13. 13)
  - N. Sureshbabu , J.A. Farrell . Wavelet-based system identification for nonlinear control. IEEE Trans. Autom. Control , 2 , 412 - 417
14. 14)
  - F.J. Lin , Y.C. Hung . FPGA-based Elman neural network control system for linear ultrasonic motor. IEEE Trans. Ultrason. Ferroelect., Freq. Control. , 1 , 101 - 113
15. 15)
  - M. Zhu . Contact analysis and mathematical modeling of traveling wave ultrasonic motors. IEEE Trans. Ultrason. Ferroelectr. Freq. Control , 6 , 668 - 679
16. 16)
  - L. Piegl , W. Tiller . (1995) The NURBS book.
17. 17)
  - X. Yue , D.M. Vilathgamuwa , K.J. Tseng . Robust adaptive control of a three-axis motion simulator with state observers. IEEE/ASME Trans. Mechatronics , 4 , 437 - 448
18. 18)
  - F.J. Lin , H.J. Shieh , P.H. Shieh , P.H. Shen . An adaptive recurrent-neural-network motion controller for x–y table in CNC machine. IEEE Trans. Sys. Man Cybern. B, Cybern. , 2 , 286 - 299
19. 19)
  - T. Sashida , T. Kenjo . (1993) An introduction to ultrasonic motors.
20. 20)
  - K. Watanabe , J. Tang , M. Nakamura , S. Koga , T. Fukuda . A fuzzy-Gaussian neural network and its application to mobile robot control. IEEE Trans. Control Syst. Technol. , 2 , 193 - 199
21. 21)
  - D. Zhao , S. Li , F. Gao , Q. Zhu . Robust adaptive terminal sliding mode-based synchronised position control for multiple motion axes systems. IET Control Theory Appl. , 1 , 136 - 150
22. 22)
  - J.L. Elman . Finding structure in time. Cogn. Sci. , 2 , 179 - 211
23. 23)
  - S. Verma , W.J. Kim , H. Shakir . Multi-axis maglev nanopositioner for precision manufacturing and manipulation applications. IEEE Trans. Ind. Appl. , 5 , 1159 - 1167
24. 24)
  - Y.F. Peng , C.M. Lin . Intelligent motion control of linear ultrasonic motor with H∞ tracking performance. IET Control Theory Appl. , 1 , 9 - 17
25. 25)
  - C.K. Chui . (1992) An introduction to wavelets.
26. 26)
  - J. Zhang , G.G. Walter , Y. Miao , W.N.W. Lee . Wavelet neural networks for function learning. IEEE Trans. Signal Process. , 6 , 1485 - 1497
27. 27)
  - M. Liu . Decentralized control of robot manipulators: nonlinear and adaptive approaches. IEEE Trans. Autom. Control , 2 , 357 - 363
28. 28)
  - J.B. Mbede , X. Huang , M. Wang . Robust neuro-fuzzy sensor-based motion control among dynamic obstacles for robot manipulators. IEEE Trans. Fuzzy Syst. , 2 , 249 - 261
29. 29)
  - F.J. Lin , P.H. Shieh , Y.C. Hung . An intelligent control for linear ultrasonic motor using interval type-2 fuzzy neural network. IET Electr. Power Appl. , 1 , 32 - 41
30. 30)
  - F.J. Lin , P.H. Shieh . Recurrent RBFN-based fuzzy neural network control for X-Y-Theta motion control stage using linear ultrasonic motors. IEEE Trans. Ultrason., Ferroelect., Freq. Control , 12 , 2450 - 2464

Recurrent wavelet-based Elman neural network control for multi-axis motion control stage using linear ultrasonic motors

Recurrent wavelet-based Elman neural network control for multi-axis motion control stage using linear ultrasonic motors

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