International Journal of Fuzzy Logic and Intelligent Systems

  • 제8권1호
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  • Pages.68-74
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  • 2008
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  • 1598-2645(pISSN)
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  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
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Neural Network Compensation Technique for Standard PD-Like Fuzzy Controlled Nonlinear Systems

  • Song, Deok-Hee (Intelligent Systems and Emotional Engineering(ISEE) Lab, BK21 Mechatronics Group Chungnam National University) ;
  • Lee, Geun-Hyeong (Intelligent Systems and Emotional Engineering(ISEE) Lab, BK21 Mechatronics Group Chungnam National University) ;
  • Jung, Seul (Intelligent Systems and Emotional Engineering(ISEE) Lab, BK21 Mechatronics Group Chungnam National University)
  • 발행 : 2008.03.01
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초록

In this paper, a novel neural fuzzy control method is proposed to control nonlinear systems. A standard PD-like fuzzy controller is designed and used as a main controller for the system. Then a neural network controller is added to the reference trajectories to form a neural-fuzzy control structure and used to compensate for nonlinear effects. Two neural-fuzzy control schemes based on two well-known neural network control schemes, the feedback error learning scheme and the reference compensation technique scheme as well as the standard PD-like fuzzy control are studied. Those schemes are tested to control the angle and the position of the inverted pendulum and their performances are compared.

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참고문헌

  1. T. H. Lee and S. S. Ge, "Intelligent Control of Mechatronic Systems", pp. 646-659, IEEE Symposium on Intelligent Control, 2003
  2. M. E. Magana and F. Holzapfel, "Fuzzy-Logic Control of an inverted pendulum with Vision Feedback", pp. 165-170, IEEE Trans. on Education, Vol. 41, No. 2, May 1998 https://doi.org/10.1109/13.669727
  3. D. Driankov, H. Hellendoorn, and M. Reinfrank, "An Introduction to Fuzzy Control", Springer, 1996
  4. T. H. Hung, M. F. Yeh, and H. C. Lu, "A PI-Like Fuzzy Controller Implementation for the Inverted Pendulum System", pp. 218-222, IEEE Conference on Intelligent Processing Systems, 1997
  5. L. X. Wang, "Adaptive Fuzzy Systems and Control", Prentice Hall, 1994
  6. J. S. Wang and C. S. Lee, "Self-Adaptive Recurrent neuro- Fuzzy Control of an Autonomous Underwater Vehicle", pp. 283-295, IEEE Trans. on Robotics and Automations, Vol. 19, No. 2, 2003 https://doi.org/10.1109/TRA.2003.808865
  7. W. Wei, S. Zeng, and X. Gan, "Fuzzy and neural network Control system of Intelligent RLED Arm Manipulators for Dynamic Obstacles", pp. 577-580, IEEE Conference on Fuzzy systems, 2001
  8. S. Pletl, "Neuro-Fuzzy Control of Rigid and Flexible Joint Robotic Manipulator", IEEE IECON , pp. 93-97, 1995
  9. A. J , P. H. Yang, D. M. Auslander, and R. N. Dave, "Real Time Neuro-Fuzzy Control of a Nonlinear Dynamic System", Biennial Conf, of North American Fuzzy Information Processing pp. 210-214, 1996
  10. K. Kiguchi, T. Fukuda, "Intelligent Position/Force Controller for Industrial Robot Manipulators - Application of Fuzzy Neural Networks", pp. 753-761, IEEE Trans. on Industrial Electronics, 1997
  11. W. Y. Lee and H. M. Choi, "Performance Improvement of Controller using Fuzzy Inference Results of System Output", pp. 77-86, Korean Fuzzy and Intelligent Systems, Vol. 5, No. 4, 1995
  12. F. L. Lewis, S. Jagannathan, and A. Yesildirek, "Neural network control of robot manipulators and nonlinear systems," Taylor and Francis, 1999
  13. M. Miyamoto, M. Kawato, T. Setoyama, and R. Suzuki, "Feedback error learning", Neural Networks, vol.1, pp. 251-265, 1988
  14. S. Jung and H. T. Cho, "Balancing and Position Tracking Control of An Inverted Pendulum on An X-Y Plane Using Decentralized Neural Networks," IEEE/ASME Advanced Intelligent Mechatronics, pp.181-186, 2003
  15. G. H. Lee, J. S. Noh, and S. Jung, "Implementation and Experiment of Neural Network Controllers for Intelligent Control System Education", International Journal of Fuzzy Logic and Intelligent Systems, pp.267-273, 2007 https://doi.org/10.5391/IJFIS.2007.7.4.267
  16. S. Jung and S. S. Kim, "Hardware implementation of a realtime neural network controller with a DSP and an FPGA for nonlinear systems", IEEE Trans. On Industrial Electronics, vol.54, no.1, pp.265-271, 2007 https://doi.org/10.1109/TIE.2006.888791

피인용 문헌

  1. Neural Network Compensation for Impedance Force Controlled Robot Manipulators vol.14, pp.1, 2014, https://doi.org/10.5391/IJFIS.2014.14.1.17
  2. Experimental Studies of Swing Up and Balancing Control of an Inverted Pendulum System Using Intelligent Algorithms Aimed at Advanced Control Education vol.14, pp.3, 2014, https://doi.org/10.5391/IJFIS.2014.14.3.200
  3. Robust Control for the Segway with Unknown Control Coefficient and Model Uncertainties vol.16, pp.7, 2016, https://doi.org/10.3390/s16071000