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Adaptive wavelet neural network controller for active suppression control of a diaphragm-type pneumatic vibration isolator

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  • Intelligent Control and Applications
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Abstract

This paper applies an intelligent control scheme to investigate a diaphragm-type pneumatic vibration isolation (PVI) problem. Adaptive wavelet neural network (AWNN) control is employed to control the PVI system which has inherited nonlinear and time-varying system characteristics. Since AWNN has excellent approximation capability originating from wavelet decomposition property and online learning ability stemming from neural networks, the method is especially suitable for the PVI control applications. In this paper, the adaptive learning rates are derived based on the Lyapunov stability theorem. Therefore, the stability of the closed-loop system can be assured. To validate the proposed method, a composite control scheme using pressure and velocity measurements as feedback signals is implemented. During experimental investigations, sinusoidal excitation with the excitation frequency close to the resonance and random-like signal are input on a floor base to simulate ground vibrations. Performances obtained from the proposed control scheme are compared with those obtained from passive isolation and PID scheme to illustrate the effectiveness of the proposed intelligent control.

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References

  1. C. G. Gordon, “Generic vibration criteria for vibrationsensitive equipment,” Proc. of SPIE, San Jose, CA, 1999.

    Google Scholar 

  2. M. Bitaraf, L. R. Barroso, and S. Hurlebaus, “Adaptive Control to Mitigate Damage Impact on Structural Response,” Journal of Intelligent Material Systems and Structures, vol. 21, no. 6, pp. 607–619, 2010.

    Article  Google Scholar 

  3. M. Bitaraf, O. Ozbulut, and S. Hurlebaus, “Application of semi-active control strategies for seismic protection of building with MR dampers,” Engineering Structures, vol. 32, no. 10, pp. 3040–3047, 2010.

    Article  Google Scholar 

  4. M. Bitaraf, S. Hurlebaus, and L. R. Barroso, “Active and Semi-active Adaptive Control for Undamaged and Damaged Building Structure under Seismic Load,” Computer-Aided Civil and Infrastructure Engineering, vol. 27, no. 1, pp. 48–64, 2012. [click]

    Article  Google Scholar 

  5. F. Kerber, S. Hurlebaus, B. M. Beadle, and U. Stöbener, “Control Concepts for an Active Vibration Isolation System,” Mechanical Systems and Signal Processing, vol. 21, no. 8, pp. 3042–3059, 2007. [click]

    Article  Google Scholar 

  6. O. Ozbulut, M. Bitaraf, and S. Hurlebaus, “Adaptive control of base-isolated structure against near-field earthquakes using variable friction dampers,” Engineering Structures, vol. 33, no. 12, pp. 3143–3154, 2011. [click]

    Article  Google Scholar 

  7. Y. H. Shin and K. J. Kim, “Performance enhancement of pneumatic vibration isolation tables in low frequency range by time delay control,” Journal of Sound and Vibration, vol. 321, no. 3-5, pp. 537–553, 2009. [click]

    Article  Google Scholar 

  8. M. C. Shih and T. Y. Wang, “Design and adaptive control of a pneumatic vibration isolator,” Proc. of International Conference of Motion and Vibration Control, vol. 6, pp. 111–116, 2002.

    Google Scholar 

  9. T. Kato, K. Kawashima, K. Sawamoto, and T. Kagawa, “Active control of a pneumatic isolation table using model following control and a pressure differentiator,” Precision Engineering, vol. 31, no. 3, pp. 269–275, 2007. [click]

    Article  Google Scholar 

  10. P. H. Chang, D. K. Han, Y. H. Shin, and K. J. Kim, “Effective suppression of pneumatic vibration isolators by using input-output linearization and time-delay control,” Journal of Sound and Vibration, vol. 329, no. 10, pp. 1632–1652, 2010.

    Article  Google Scholar 

  11. C. H. Lin, “Hybrid recurrent wavelet neural network control of PMSM servo-drive system for electric scooter,” International Journal of Control, Automation and Systems, vol. 12, no. 1, pp. 177–187, 2014. [click]

    Article  Google Scholar 

  12. S. Jagannathan and P. He, “Neural-network-based state feedback control of a nonlinear discrete-time system in nonstrict feedback form,” IEEE Trans. on Neural Networks., vol. 19, no. 12, pp. 2073–2087, 2008.

    Article  Google Scholar 

  13. N. T. Tai and K. K. Ahn, “A RBF neural network sliding mode controller for SMA actuator,” International Journal of Control, Automation and Systems, vol. 8, no. 6, pp. 1296–1305, 2010. [click]

    Article  Google Scholar 

  14. L. Yu, S. Fei, and X. Li, “RBF neural networks-based robust adaptive tracking control for switched uncertain nonlinear systems,” International Journal of Control, Automation and Systems, vol. 10, no. 2, pp. 437–443, 2012. [click]

    Article  Google Scholar 

  15. F. J. Lin, H. J. Shieh, and P. K. Huang, “Adaptive wavelet neural network control with hysteresis estimation for piezo-positioning mechanism,” IEEE Trans. on Neural Networks, vol. 17, no. 2, pp. 432–444, 2006.

    Article  Google Scholar 

  16. J. X. Xu and Y. Tan, “Nonlinear adaptive wavelet control using constructive wavelet networks,” IEEE Trans. on Neural Networks, vol. 18, no. 1, pp. 115–127, 2007.

    Article  MathSciNet  Google Scholar 

  17. F. F. M. EI-Sousy, “Hybrid H ∞-based wavelet-neuralnetwork tracking control for permant-magnet synchronous motor servo drive,” IEEE Trans. on Industrial Electronics., vol. 57, no. 9, pp. 3157–3166, 2010.

    Article  Google Scholar 

  18. M. Khan and M. A. Rahman, “A novel neuro-waveletbased self-tuned wavelet controller for IPM motor drives,” IEEE Trans. on Industry Applications, vol. 46, no. 3, pp. 1194–1203, 2010.

    Article  Google Scholar 

  19. F. J. Lin, Y. C. Hung, and S. Y. Chen, “Field-programmable gate array-based intelligent dynamic sliding-mode control using recurrent wavelet neural network for linear ultrasonic motor,” IET Control Theory and Applications, vol. 4, no. 9, pp. 1511–1532, 2010.

    Article  Google Scholar 

  20. Q. Zhang, G. G. Walter, Y. Miao, and W. N. W. Lee, “Wavelet neural networks for function learning,” IEEE Trans. on Signal Processing, vol. 43, no. 6, pp. 1485–1497, 1995.

    Article  Google Scholar 

  21. C. F. Hsu, C. M. Lin, and T. T. Lee, “Wavelet adaptive backstepping control for a class of nonlinear systems,” IEEE Trans. on Neural Networks, vol. 17, no. 5, pp. 1175–1183, 2006.

    Article  Google Scholar 

  22. F. F. M. EI-Sousy, “Robust wavelet-neural-network sliding-mode control system for permanent magnet synchronous motor drive,” IET Electric Power Applications, vol. 5, no. 1, pp. 113–132, 2011.

    Article  Google Scholar 

  23. P. H. Chang, D. K. Han, Y. H. Shin, and K. J. Kim, “Effective suppression of pneumatic vibration isolators by using input-output linearization and time delay control,” Journal of Sound and Vibration, vol. 329, no. 10, pp. 1636–1652, 2010. [click]

    Article  Google Scholar 

  24. C. Erin and B. Wilson, “An improved model of the pneumatic vibration isolator: theory and experiment,” Journal of Sound and Vibration, vol. 218, no. 1, pp. 81–101, 1998. [click]

    Article  Google Scholar 

  25. R. J.Wai and J. M. Chang, “Intelligent control of induction servo motor drive via wavelet neural network,” Electric Power Systems Research, vol. 61, no. 1, pp. 67–76, 2002. [click]

    Article  Google Scholar 

  26. M. Farahani, “Intelligent control of SVC using wavelet neural network to enhance transient stability,” Engineering Applications of Artificial Intelligence, vol. 26, no. 1, pp. 273–280, 2013. [click]

    Article  MathSciNet  Google Scholar 

  27. D. Stathakis, “How many hidden layers and nodes,” International Journal of Remote Sensing, vol. 30, no. 8, pp. 2133–2147, 2009. [click]

    Article  Google Scholar 

  28. K. Gnana Sheela and S. N. Deepa, “Review of methods to fix number of hidden neurons in neural networks,” Mathematical Problems in Engineering, vol. 2013, Article ID 425740, 2013. [click]

    Google Scholar 

  29. G. Priyandoko, M. Mailah, and H. Jamaluddin, “Vehicle active suspension system using skyhook adaptive neuro active force control,” Mechanical System and Signal Processing, vol. 23, no. 3, pp. 855–868, 2009. [click]

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Mechanical Engineering, Ming Chi University of Technology, No. 84, Gungjuan Road, Taishan, New Taipei City, 243, Taiwan

    Hung-Yi Chen & Jin-Wei Liang

Authors
  1. Hung-Yi Chen
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  2. Jin-Wei Liang
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Corresponding author

Correspondence to Hung-Yi Chen.

Additional information

Recommended by Associate Editor Hongbo Li under the direction of Editor Fuchun Sun. The authors would like to acknowledge the financial support of the National Science Council of TAIWAN through its grant NSC-101-2221-E-131-008.

Hung-Yi Chen received the M.S. degree from Auburn University, AL., U.S.A. and the Ph.D. degree from National Taiwan University of Science and Technology, Taiwan, in 1991 and 2006, respectively, all in mechanical engineering. Since 1997, he has been with Ming Chi University of Technology, where he is currently an associate professor. His research interests include intelligent control applications, mechatronics, automation and vehicle suspension control.

Jin-Wei Liang received the B.Sc. and M.Sc. degrees from National Taiwan University of Science and Technology, Taiwan, in 1985 and 1988, respectively. He got his Ph.D. degree from Michigan State University, U.S.A., in 1996. All his majors are in mechanical engineering. In 1985, he joined the faculty of the Department of Mechanical Engineering, Ming Chi University of Technology, Taiwan. He is currently a professor at the Mech. Eng. Dept. His research interests include nonlinear dynamics, vibration and control.

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Chen, HY., Liang, JW. Adaptive wavelet neural network controller for active suppression control of a diaphragm-type pneumatic vibration isolator. Int. J. Control Autom. Syst. 15, 1456–1465 (2017). https://doi.org/10.1007/s12555-014-0428-2

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  • DOI: https://doi.org/10.1007/s12555-014-0428-2

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