DOI QR코드

DOI QR Code

Neuro-fuzzy Control for Balancing a Two-wheel Mobile Robot

이륜구동 이동로봇의 균형을 위한 뉴로 퍼지 제어

  • Park, Young Jun (Department of Mechatronics Engineering, Chungnam National University) ;
  • Jung, Seul (Department of Mechatronics Engineering, Chungnam National University)
  • 박영준 (충남대학교 메카트로닉스공학과) ;
  • 정슬 (충남대학교 메카트로닉스공학과)
  • Received : 2015.06.08
  • Accepted : 2015.12.16
  • Published : 2016.01.01

Abstract

This paper presents the neuro-fuzzy control method for balancing a two-wheel mobile robot. A two-wheel mobile robot is built for the experimental studies. On-line learning algorithm based on the back-propagation(BP) method is derived for the Takagi-Sugeno(T-S) neuro-fuzzy controller. The modified error is proposed to learn the B-P algorithm for the balancing control of a two-wheel mobile robot. The T-S controller is implemented on a DSP chip. Experimental studies of the balancing control performance are conducted. Balancing control performances with disturbance are also conducted and results are evaluated.

Keywords

References

  1. Segway, http://segway.com
  2. H. W. Kim and S. Jung, "Balancing control of a two-wheel mobile robot," ROBOTICA, to be printed in 2016.
  3. H. W. Kim, S. T. Cho, and S. Jung, "Implementation and balancing control of a robotic vehicle for entertainment," Journal of Institute of Control, Robotics and Systems (in Korean), pp. 736-740, vol. 20, no. 7, 2014. https://doi.org/10.5302/J.ICROS.2014.13.8012
  4. H. J. Lee and S. Jung, "Balancing and navigation control of a mobile inverted pendulum robot using sensor fusion of low cost sensors," Mechatronics, vol. 22, no. 1, pp. 95-105, 2012. https://doi.org/10.1016/j.mechatronics.2011.11.011
  5. J. S. Noh, G. H. Lee, and S. Jung, "Position control of a mobile inverted pendulum system using radial basis function network," International Journal of Control, Robotics, and Systems, vol. 8, no. 1, pp. 157-162, 2012.
  6. C. H. Huang, W. J. Wang, and C. H. Chiu, "Design and implementation of fuzzy control on a two-wheel inverted pendulum system," IEEE Trans. on Industrial Electronics, vol. 58, no. 7, pp. 2988-3001, 2011. https://doi.org/10.1109/TIE.2010.2069076
  7. G. H. Lee and S. Jung, "Control of inverted pendulum system using a neuro-fuzzy controller for intelligent control education," IEEE ICMA, pp. 965-970, 2008.
  8. G. H. Lee and S. Jung, "Design and control of an inverted pendulum system for intelligent mechatronics system control education," IEEE AIM, pp. 1254-1259, 2008.
  9. C. T. Lin, "A neural fuzzy control system with structure and parameter learning," Fuzzy Sets and Systems, pp. 183-212, 1995.
  10. J. R. Jang, "ANFIS: adaptive-network-based fuzzy inference system," IEEE Transactions on Systems Man and Cybernetics, pp. 665-685, 1993.
  11. H. Cao, Y. Wang, L. Jia, G. Si, and Y. Zhang, "Generalized Tagaki-Sugeno fuzzy rules based prediction model with application to power plant pulverizing system," IEEE CDC, pp. 7409-7414, 2013.
  12. C. C. Chuang. C. C. Hsiao, and J. Y. Jeng, "Adaptive fuzzy regression clustering algorithm for TSK fuzzy modeling," CIRA, pp. 201-206, Jul. 2003.
  13. G. Hernandez and G. Lachiver, "Biunivocal relation between TSK fuzzy controller and PID controller and, guarantee and manipulation of the stability for the proposed fuzzy controller," CCECE, pp. 562-566, Ottawa, May 2006.
  14. Y. J. Park and S. Jung, "Balancing control of a two-wheel mobile robot," Proc. of 2015 30th ICROS Annual Conference (in Korean), Daejeon, pp.369-370, May 2015.