International Journal of Fuzzy Logic and Intelligent Systems

Korean Institute of Intelligent Systems (한국지능시스템학회)
권호 표지
DOI QR코드

DOI QR Code

Implementation and Experiment of Neural Network Controllers for Intelligent Control System Education

  • Lee, Geun-Hyeong (Intelligent Systems and Emotional Engineering(ISEE) Lab, BK21 Mechatronics Group, Chungnam National University) ;
  • Noh, Jin-Seok (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)
  • Published : 2007.12.01
Download PDF
⟨ Previous Next ⟩

Abstract

This paper presents the implementation of an educational kit for intelligent system control education. Neural network control algorithms are presented and control hardware is embedded to control the inverted pendulum system. The RBF network and the MLP network are implemented and embedded on the DSP 2812 chip and other necessary functions are embedded on an FPGA chip. Experimental studies are conducted to compare performances of two neural control methods. The intelligent control educational kit(ICEK) is implemented with the inverted pendulum system whose movements of the cart is limited by space. Experimental results show that the neural controllers can manage to control both the angle and the position of the inverted pendulum systems within a limited distance. Performances of the RCT and the FEL control method are compared as well.

Keywords

References

  1. F. L. Lewis, S. Jagannathan, and A. Yesildirek, 'Neural network control of robot manipulators and nonlinear systems', Taylor & Francis, 1999
  2. M. T. Hagan, C. D. Latino, E. Misawa, and G. Young, 'An interdisciplinary control systems laboratory', IEEE Conference on Control Applications, pp. 403-408, 1996
  3. I. Fantoni and R. Lozano, 'Global stabilization of the cartpendulum system using saturation functions', IEEE Conference on Decision and Control, pp. 4393-4398, 2003
  4. R. Yang, Y. Y. Kuen, and Z. Li, 'Stabilization of a 2-DOF spherical pendulum on x-y table', IEEE Conference on Control Applications, pp. 724-729, 2000
  5. R. J. Wai, J. D. Lee, and L. J. Chang, 'Development of adaptive sliding mode control for nonlinear dual-axis inverted-pendulum system', IEEE/ASME Conference on AdvancedIntelligent Mechatronics, pp. 815-820,2003
  6. T. Lahdhiri, C. Carnal, and A. Alouani, 'Cart-pendulum balancing problem using fuzzy logic control', Proceedings of Southeastern Can! 1994, pp. 393-397
  7. M. E. Magana and F. Holzapfel, 'Fuzzy-logic control of an inverted pendulum with vision feedback', IEEE Trans. on Education, vol. 41, no. 2, pp. 165-170, 1998 https://doi.org/10.1109/13.669727
  8. T. H. Hung, M. F. Yeh, and H. C. Lu, 'A pi-like fuzzy controller implementation for the inverted pendulum system,' Proc. of IEEE Conference on Intelligent Processing Systems, 1997, pp. 218-222
  9. S. Omatu, T. Fujinaka, and M. Yoshioka, 'Neuro-pid control for inverted single and double pendulums,' IEEE Conf. On Systems, Man, and Cybernetics, 2000, pp. 8-11
  10. S. Jung and S. B. Yim, 'Reference compensation technique using neural network for controlling large x-y table robot,' International Symposium on Robotics and Automations, 2000, pp. 461-466
  11. M. Miyamoto, M. Kawato, T. Setoyama, and R. Suzuki, 'Feedback error learning', Neural Networks, vol.1, pp. 251-265, 1988
  12. H. T. Cho and S. Jung, 'Balancing and position tracking control of an inverted pendulum on an X-Y plane using decentralized neural networks,' IEEE/ASME Conference on Advanced Intelligent Mechatronics, 2003, pp.181-186
  13. S. Jung and H. T. Cho, 'Decentralized neural network reference compensation technique for PD controlled two degrees-of-freedom inverted pendulum,' International Journal of Control, Automations, and System, vol. 2, no. 1, pp.92-99, 2004

Cited by

  1. Linear State Feedback Regulation of a Furuta Pendulum: Design Based on Differential Flatness and Root Locus vol.4, pp.2169-3536, 2016, https://doi.org/10.1109/ACCESS.2016.2637822