Table of Contents Author Guidelines Submit a Manuscript
Shock and Vibration
Volume 2016 (2016), Article ID 2860596, 9 pages
http://dx.doi.org/10.1155/2016/2860596
Research Article

Self-Organizing Adaptive Wavelet Backstepping Control Research for AC Servo System

Department of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, China

Received 8 September 2015; Revised 27 October 2015; Accepted 28 October 2015

Academic Editor: Vadim V. Silberschmidt

Copyright © 2016 Run-min Hou et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. J. Zhou and Y. Wang, “Adaptive backstepping speed controller design for a permanent magnet synchronous motor,” IEE Proceedings: Electric Power Applications, vol. 149, no. 2, pp. 165–172, 2002. View at Publisher · View at Google Scholar · View at Scopus
  2. S. Zhu, M. X. Sun, and X. X. He, “Iterative learning control of strict-feedback nonlinear time-varying systems,” Acta Automatica Sinica, vol. 36, no. 3, pp. 454–458, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  3. Y.-P. Sun, J.-M. Li, and J.-A. Wang, “Adaptive learning control of nonlinear systems with iteration-varying trajectory,” Systems Engineering and Electronics, vol. 31, no. 7, pp. 1715–1719, 2009. View at Google Scholar · View at Scopus
  4. X.-L. He and S.-C. Tong, “Direct adaptive fuzzy backstepping control of nonlinear systems with dynamic uncertainties,” Control Theory and Applications, vol. 26, no. 10, pp. 1081–1086, 2009. View at Google Scholar · View at Scopus
  5. C.-F. Hsu, C.-M. Lin, and T.-T. Lee, “Wavelet adaptive backstepping control for a class of nonlinear systems,” IEEE Transactions on Neural Networks, vol. 17, no. 5, pp. 1175–1183, 2006. View at Publisher · View at Google Scholar · View at Scopus
  6. D. Liu and M. Li, “PMSM position servo control based on wavelet neural network adaptive backstepping,” Electric Power Automation Equipment, vol. 33, no. 2, pp. 126–130, 2013. View at Publisher · View at Google Scholar · View at Scopus
  7. C.-M. Lin, K.-N. Hung, and C.-F. Hsu, “Adaptive neuro-wavelet control for switching power supplies,” IEEE Transactions on Power Electronics, vol. 22, no. 1, pp. 87–95, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. J. S. Albus, “A new approach to manipulator control: the cerebellar model articulation controller (CMAC),” Journal of Dynamic Systems, Measurement and Control, vol. 97, no. 3, pp. 220–227, 1975. View at Google Scholar
  9. C.-M. Lin and Y.-F. Peng, “Adaptive CMAC-based supervisory control for uncertain nonlinear systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 34, no. 2, pp. 1248–1260, 2004. View at Publisher · View at Google Scholar · View at Scopus
  10. Y.-F. Peng, “Robust intelligent backstepping control system using RCMAC for tracking periodic trajectories,” Nonlinear Analysis: Real World Applications, vol. 12, no. 3, pp. 1371–1385, 2011. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  11. C.-M. Lin, L.-Y. Chen, and C.-H. Chen, “RCMAC hybrid control for MIMO uncertain nonlinear systems using sliding-mode technology,” IEEE Transactions on Neural Networks, vol. 18, no. 3, pp. 708–720, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. C. T. Ching and C. S. Lin, “CMAC with general basis functions,” Neural Networks, vol. 9, no. 7, pp. 1199–1211, 1996. View at Publisher · View at Google Scholar · View at Scopus
  13. H.-M. Lee, C.-M. Chen, and Y.-F. Lu, “A self-organizing HCMAC neural-network classifier,” IEEE Transactions on Neural Networks, vol. 14, no. 1, pp. 15–27, 2003. View at Publisher · View at Google Scholar · View at Scopus
  14. C.-M. Lin and H.-Y. Li, “Self-organizing adaptive wavelet CMAC backstepping control system design for nonlinear chaotic systems,” Nonlinear Analysis: Real World Applications, vol. 14, no. 1, pp. 206–223, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  15. L. Wu, X. Su, P. Shi, and J. Qiu, “Model approximation for discrete-time state-delay systems in the T-S fuzzy framework,” IEEE Transactions on Fuzzy Systems, vol. 19, no. 2, pp. 366–378, 2011. View at Publisher · View at Google Scholar · View at Scopus
  16. Y.-G. Leu, T.-T. Lee, and W.-Y. Wang, “Observer-based adaptive fuzzy-neural control for unknown nonlinear dynamical systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics, vol. 29, no. 5, pp. 583–591, 1999. View at Publisher · View at Google Scholar · View at Scopus
  17. W. Zhang, W. Liu, X. Ye, Y. Zhu, and X. Hu, “Robust adaptive control for free-floating space manipulators based on neural network,” Journal of Mechanical Engineering, vol. 48, no. 21, pp. 36–40, 2012. View at Publisher · View at Google Scholar · View at Scopus
  18. Y.-M. Fang, S.-C. Ren, Z.-J. Wang, and X.-H. Jiao, “Adaptive fuzzy backstepping control for speed of permanent magnet synchronous motor,” Electric Machines and Control, vol. 15, no. 6, pp. 97–102, 2011. View at Google Scholar · View at Scopus