Journal of the Korea Institute of Information and Communication Engineering (한국정보통신학회논문지)

The Korea Institute of Information and Commucation Engineering (한국정보통신학회)
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Implementation of a Fuzzy Control System for Two-Wheeled Inverted Pendulum Robot based on Artificial Neural Network

인공신경망에 기초한 이륜 역진자 로봇의 퍼지 제어시스템 구현

  • 정건우 (부산대학교 대학원 전자전기공학과) ;
  • 최영규 (부산대학교 전자전기공학부)
  • Received : 2012.08.17
  • Accepted : 2012.09.19
  • Published : 2013.01.31
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Abstract

In this paper, a control system for two wheeled inverted pendulum robot is implemented to have more stable balancing capability than the conventional control system. Fuzzy control structure is chosen for the two wheeled inverted pendulum robot, and fuzzy membership function factors for the control system are obtained for 3 specified weights using a trial-and-error method. Next a neural network is employed to generate fuzzy membership function factors for more stable control performance when the weight is arbitrarily selected. Through some experiments, we find that the proposed fuzzy control system using the neural network is superior to the conventional fuzzy control system.

본 논문에서는 친환경 이동 수단인 이륜 역진자 로봇을 기존의 방법보다 더욱 안정적으로 밸런싱 하기 위한 제어시스템을 구현하였다. 먼저 이륜 역진자 로봇의 제어시스템을 퍼지 제어구조로 선택하고, 적절한 소속함수 요소 값들을 지정된 3종류의 무게에 따라 시행착오적으로 구하였다. 임의의 무게에 따른 퍼지 소속 함수 요소 값을 구하기 위해 3종류의 무게에 따른 퍼지 소속함수 요소 값을 신경회로망으로 튜닝한 뒤 퍼지 제어시스템에 적용하여 보다 안정적인 제어가 가능 하도록 제어시스템을 구현하였다. 구현된 제어시스템을 실제 로봇에 적용시켜 본 결과, 기존의 퍼지 제어시스템에 비해서 본 논문에서 제안한 신경회로망으로 튜닝한 퍼지 제어시스템이 보다 우수함을 확인할 수 있었다.

Keywords

References

  1. H. Azizan andM. Jafarinasab, "Fuzzy control based on LMI approach and fuzzy interpretation of the rider input for two wheeled balancing human transporter," IEEE Proceedings of the 8th International Conference on Control and Automation, pp. 192-197, 2010.
  2. K.M. Goher andM. O. Tokhi, "Anewconfiguration of two wheeled vehicles: Towards a more workspace and motion flexibility," IEEE Proceedings of the 4th International Systems Conference, pp. 524-528, 2010.
  3. W. Zhong and H. Rock, "Energy and passivity based control of the double inverted pendulum on a cart," IEEE Proceedings of the International Conference on Control Applications, pp. 896-901, 2001.
  4. F. Grasser, A. D'Arrigo, S. Colombi and A. C. Ruffer, "JOE: A mobile, inverted pendulum," IEEE Transactions on Industrial Electronics, vol. 49, no. 1, pp. 107-114, 2002. https://doi.org/10.1109/41.982254
  5. D. Voth, "Segway to the future autonomous mobile robot," IEEE Intelligent Systems, vol. 20, no. 3, pp. 5-8, 2005.
  6. K. Pathak, J. Franch and S. K. Agrawal, "Velocity and position control of a wheeled inverted pendulum by partial feedback linearization," IEEE Transactions on Robotics, vol. 21, no. 3, pp. 505-513, 2005. https://doi.org/10.1109/TRO.2004.840905
  7. Ching-Chih Tsai, Hsu-Chih, and Shui-Chun Lin, "Adaptive neural network control of a self-balancing two-wheeled scooter," IEEE Transactions on Industrial Electronics, vol. 57, no. 4, April 2010.
  8. Akira Shimada and Naoya Hatakeyama, "Movement control of two-wheeled inverted pendulum robots considering robustness," SICE Annual Conference, Japan, August 20-22, 2008.
  9. Seul Jung, and Sung Su Kim, "Control experiment of a wheel-driven mobile inverted pendulum using neural network," IEEE Transactions on Control Systems Technology, vol. 16, no. 2, March 2008.
  10. 김현욱, 정슬, "무게 변화에 따른 차륜형 밸런싱 로봇의제어기설계및실험연구,"한국지능 시스템학회논문지, vol. 20, no. 4, pp. 469-475, 2010.
  11. K. M. Goher, M. O. Tokhi and N. H. Siddique, "Dynamic modeling and control of a two wheeled robotic vehicle with a virtual payload," ARPN Journal of Engineering and Applied Sciences, vol. 6, no. 3, March 2011.
  12. http://www.segway.com/

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