A Neural Fuzzy System for Vibration Control in Flexible Structures
- 1
- 2
Abstract
An adaptive neural fuzzy (NF) controller is developed in this paper for active vibration suppression in flexible structures. A recurrent identification network (RIN) is developed to adaptively identify system dynamics of the plant. A novel recurrent training (RT) technique is suggested to train the RIN so as to optimize nonlinear input-output mapping and to enhance convergence. The effectiveness of the developed controller and the related techniques has been verified experimentally corresponding to different control scenarios. Test results show that the proposed RIN can effectively recognize the time-varying dynamics of the plant. The RT-based hybrid training technique can improve the adaptive capability of the control system to accommodate different system conditions and enhance the training convergence. The developed NF controller is a robust and stable vibration suppression system, and it outperforms other related NF controllers.
- D. Younesian, E. Esmailzadeh and R. Sedaghati, “Passive Vibration Control of Beams Subjected to Random Excitations with Peaked PSD,” Journal of Vibration and Control, Vol. 12, No. 9, 2006, pp. 941-953. doi:10.1177/1077546306068060
- G. Genta, “Vibration Dynamics and Control,” Springer, New York, 2009. doi:10.1007/978-0-387-79580-5
- Z. Mohamed, J. Martins, M. Tokhi, J. Costa and M. Botto, “Vibration Control of a Very Flexible Manipulator System,” Control Engineering Practice, Vol. 13, No. 3, 2005, pp. 267-277. doi:10.1007/978-0-387-79580-5
- K. Melhem and W. Wang, “Global Output Tracking Control of Flexible Joint Robots via Factorization of the Mass Matrix,” IEEE Transactions on Robotics, Vol. 25, No. 2, 2009, pp. 248-257. doi:10.1007/978-0-387-79580-5
- M. Ishikawa, Y. Minami and T. Sugie, “Development and Control Experiment of the Trident Snake Robot,” IEEE/ ASME Transactions on Mechatronics, Vol. 15, No. 1, 2010, pp. 9-16. doi:10.1109/TMECH.2008.2011985
- R. Jamil, “Inverse Dynamics Based Tuning of a Fuzzy Logic Controller for a Single-Link Flexible Manipulator,” Journal of Vibration and Control, Vol. 13, No. 12, 2007, pp. 1741-1759. doi:10.1177/1077546307076282
- T. Li and S. Tsai, “T-S Fuzzy Bilinear Model and Fuzzy Controller Design for a Class of Nonlinear Systems,” IEEE Transactions on Fuzzy Systems, Vol. 15, No. 3, 2007, pp. 494-506. doi:10.1109/TFUZZ.2006.889964
- C. Ting, “An Observer-Based Approach to Controlling Time-Delay Chaotic Systems via Takagi-Sugeno Fuzzy Model,” Information Sciences, Vol. 177, No. 20, 2007, pp. 4314-4328. doi:10.1109/TFUZZ.2006.889964
- L. Tian and C. Collins, “A Dynamic Recurrent Neural Network-Based Controller for a Rigid-Flexible Manipulator System,” Mechatronics, Vol. 14, No. 5, 2004, pp. 471-490. doi:10.1109/TFUZZ.2006.889964
- M. Caswara and H. Unbehauen, “A Neurofuzzy Approach to the Control of a Flexible-Link Manipulator,” IEEE Transaction on Robotics and Automation, Vol. 18, No. 6, 2002, pp. 932-944. doi:10.1109/TRA.2002.805660
- L. Tian and C. Collins, “Adaptive Neuro-Fuzzy Control of a Flexible Manipulator,” Mechatronics, Vol. 15, No. 10, 2005, pp. 1305-1320. doi:10.1016/j.mechatronics.2005.02.001
- W. Wang and O. Jianu, “A Smart Monitor for Machinery Fault Detection,” IEEE/ASME Transactions on Mechatronics, Vol. 15, No. 1, 2010, pp. 70-78. doi:10.1109/TMECH.2009.2016956