International Journal of Fuzzy Logic and Intelligent Systems

  • 제4권1호
  • /
  • Pages.63-69
  • /
  • 2004
  • /
  • 1598-2645(pISSN)
  • /
  • 2093-744X(eISSN)
한국지능시스템학회 (Korean Institute of Intelligent Systems)
권호 표지
DOI QR코드

DOI QR Code

Improvement of Control Performance by Data Fusion of Sensors

  • Na, Seung-You (Department of Electronics, Computer and Information Engineering Chonnam National University) ;
  • Shin, Dae-Jung (Department of Electronics, Computer and Information Engineering Chonnam National University)
  • 발행 : 2004.06.01
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

In this paper, we propose a general framework for sensor data fusion applied to control systems. Since many kinds of disturbances are introduced to a control system, it is necessary to rely on multisensor data fusion to improve control performance in spite of the disturbances. Multisensor data fusion for a control system is considered a sequence of making decisions for a combination of sensor data to make a proper control input in uncertain conditions of disturbance effects on sensors. The proposed method is applied to a typical control system of a flexible link system in which reduction of oscillation is obtained using a photo sensor at the tip of the link. But the control performance depends heavily on the environmental light conditions. To overcome the light disturbance difficulties, an accelerometer is used in addition to the existing photo sensor. Improvement of control performance is possible by utilizing multisensor data fusion for various output responses to show the feasibility of the proposed method in this paper.

키워드

참고문헌

  1. Janice C.-Y. Yang and David W. Clarke, 'A Self-Validating Thermocouple,' IEEE Trans On Control Systems Technology, Vol. 5, No.2, pp. 239-253, March 1997 https://doi.org/10.1109/87.556028
  2. S. Y. Na and H. J. Bae, 'Design and Implementation of Intelligent Digital Controllers with Self-validating Sensors,' Korea Information Processing Society, Vol. 7, No. 12, pp. 3848-3854, Dec. 2000
  3. F. Russo and G. Rarnponi 'Fuzzy Methods for Multisensor Data Fusion,' IEEE Trans. Instrumentation and Measurement, Vol. 43, No.2, pp. 288-294, 1994 https://doi.org/10.1109/19.293435
  4. G. A. Medrano-Cerda, 'Robust Computer Control of an Inverted Pendulum,' IEEE Control Systems, Vol. 19, No.4, pp. 58-67, June 1999 https://doi.org/10.1109/37.768541
  5. K. L. Cho, Y. H. Joo and J. B. Park, 'Intelligent Digital Redesin for Dynamical Systems with Uncertainties,' Journal of Fuzzy logic and Intelligent Systems, Vol. 13, No.6, pp. 667-672, Dec. 2003 https://doi.org/10.5391/JKIIS.2003.13.6.667
  6. Yongtae Do, 'A technique to Reduce Measurement Uncertainty Using Multisensors,' Journal of the Korean Sensors Society, Vol. 4, No.4, pp. 47-54, Nov. 1995
  7. Seongjin Choi, 'Suction Detection in Left Ventricular Assist System: Data Fusion Approach,' International Journal of Control, Automation, and Systems, Vol. 1, No.3, pp. 368-375, September. 2003
  8. L. Wald, 'Some terms of reference in data fusion,' IEEE Trans. on Geoscience and Remote Sensing, Vol. 37, No. 3, 1999
  9. Y. F. Li and X. B. Chen, 'End-Point Sensing and State Observation of a Flexible-Link Robot,' IEEE/ASME Trans. on Mechatronics, Vol. 6, No. 3, pp. 351-356, September. 2001 https://doi.org/10.1109/3516.951373
  10. D. Hall, Mathematical Techniques in Multisensor Data Fusion, Norwood, MA, 1992
  11. J. W. Howze and S. P. Bhattachaiyya, 'Robust Tracking, Error Feedback, and Two-Degree-of-Freedom Controllers,' IEEE Trans. on Automatic Control, Vol. 42, No. 7, pp. 980-983, July. 1997 https://doi.org/10.1109/9.599977
  12. S. Y. Na and D. J. Shin, 'Acceleration Sensor Based Measurement and Noise Reducdon of Dynamic Weights,' Journal of the Korean Sensors Society, Vol. 13, No. 1, pp. 1-11, Jan. 2004 https://doi.org/10.5369/JSST.2004.13.1.001