Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Fuzzy-Neural Modeling of a Human Operator Control System

인간 운용자 제어시스템의 퍼지-뉴럴 모델링

  • Published : 2007.05.01
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Abstract

This paper presents an application of intelligent modeling method to manual control system with human operator. Human operator as a part of controller is difficult to be modeled because of changes in individual characteristics and operation environment. So in these situation, a fuzzy model developed relying on the expert's experiences or trial and error may not be acceptable. To supplement the fuzzy model block, a neural network based modeling error compensator is incorporated. The feasibility of the present fuzzy-neural modeling scheme has been investigated for the real human based target tracking system.

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References

  1. D. T. McRuer and H. R. Jex, 'A review of quasi-linear pilot models,' IEEE Transactions on Human Factors in Electronics, vol. 8, pp. 231-249, Sept. 1967 https://doi.org/10.1109/THFE.1967.234304
  2. D. L. Kleinman, S. Baron, and W. H. Levison, 'An optimal control model of human response, part 1: Theory and validation,' Automatica, vol. 6, 1970
  3. D. T. McRuer, 'Human dynamics in man-machine systems,' Automatica, vol. 16, 1980
  4. J. M. E. Van De Vegte, P. Milgram, and R H. Kwong, 'Teleoperator control models: Efects of time delay and imperfect system knowlegde,' IEEE Transactions on Systems Man Cybernetics, vol. 20, no. 6, pp. 1258-1272, Nov./Dec. 1990 https://doi.org/10.1109/21.61199
  5. M. R Anderson, 'A model of the human operator using sensitivity function shaping,' Proc. of the American Control Conference, June 1994
  6. S. Lee and H. S. Lee, 'Modeling, design, and evaluation of advanced teleoperator control systems with short time delay,' IEEE Trans. on Robotics and Automation, vol. 9, no. 5, October 1993
  7. T. Takagi and M. Sugeno, 'Fuzzy identification of systems and its applications to modeling and control,' IEEE Transactions on Systems Man Cybernetics, vol. 15, no. 1, pp. 116-132, 1985
  8. M. Sugeno and T. Yasukawa, 'A fuzzy-logie-based approach to qualitative modeling,' IEEE Transactions on Fuzzy Systems, vol. 1, no. I, pp. 7-31, Feb. 1993 https://doi.org/10.1109/TFUZZ.1993.390281
  9. E. Kim, M. Park:, S. Ji, and M. Park, 'A new approach to fuzzy modeling,' IEEE Transations on Fuzzy Systems, vol. 5, no. 3, August 1997
  10. S. Shao, 'Fuzzy self-organizing controller and its application for dynamic processes,' Fuzzy sets and systems, vol. 26, pp. 151-164, 1988 https://doi.org/10.1016/0165-0114(88)90205-9
  11. R. Tanscheit and E. M. Scharf, 'Experiments with the use of a rule-based self-organizing controller for robotics applications,' Fuzzy sets and systems, vol. 26, pp. 195-214, 1988 https://doi.org/10.1016/0165-0114(88)90208-4
  12. M. Sugeno and K. T. Kang, 'Structure identification of fuzzy model,' Fuzzy sets and systems, vol. 28, pp. 15-33, 1988 https://doi.org/10.1016/0165-0114(88)90113-3
  13. L. X. Wang, 'Stable adaptive fuzzy control of nonlinear systems,' IEEE Transations on Fuzzy Systems, vol. 1, no. 2, May 1993
  14. S. Horikawa, T. Furuhashi, and Y. Uchikawa, 'On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm,' IEEE Transactions on Neural Networks, vol. 3, no. 5, Sept. 1992
  15. J. S. Roger Jang and C. T. Sun, 'Functional equivalence between radial basis function networks and fuzzy inference systems,' IEEE Transactions on Neural Networks, vol. 4, no. 1, Jan. 1993
  16. J. S. Roger Jang, 'ANFIS: Adaptive-network-based fuzzy inference system,' IEEE Transactions on Systems Man Cybernetics, vol. 23, no. 3, May 1993
  17. Z. L. Liu and 1. Svoboda, 'A new control scheme for nonlinear systems with disturbances,' IEEE Transactions on Control Systems technology, vol. 14, no. 1, January 2006