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IJAT Vol.7 No.5 pp. 476-481
doi: 10.20965/ijat.2013.p0476
(2013)

Review:

Sensing and Control of Friction in Positioning

Masatake Shiraishi* and Hideyasu Sumiya**

*Open University, Ibaraki Study Center, 2-1-1 Bunkyo, Mito, Ibaraki 310-0056, Japan

**Faculty of Engineering, Ibaraki University, 4-12-1 Nakanarusawa, Hitachi, Ibaraki 312-8511, Japan

Received:
June 5, 2013
Accepted:
July 8, 2013
Published:
September 5, 2013
Keywords:
friction detection, sensor fusion, AE sensor, disturbance control
Abstract

Robust sensing and control in fine positioning is a key technology in the presence of various disturbances. For example, the position accuracy of high performance motion control systems is adversely affected by vibrations due to compliance and nonlinear effects such as friction. This paper focuses on a robust friction sensingmethodology based on the sensor fusion via the neural network from AE (acoustic emission) sensors and the feedforward control for the compensation of friction. This compensation is found to be useful for positioning control when frictions applied to the system are adequately and robustly estimated. Encouraging transient response and steady-state control performance were observed in the experimental results of positioning control of a one-dimensional transmission mechanism. The proposed friction sensing and feedforward control can be applied without modifications for nanoscale positioning.

Cite this article as:
M. Shiraishi and H. Sumiya, “Sensing and Control of Friction in Positioning,” Int. J. Automation Technol., Vol.7, No.5, pp. 476-481, 2013.
Data files:
References
  1. [1] M. Shiraishi and H. Sumiya, “Improvement of Geometrical Errors by Surface Roughness and Tool Position Controls,” ASME, Modeling of Machine Tools Symp., PED-Vol.45, 1990, pp. 9-22.
  2. [2] A. Agogino and S. Srinvas, “Multiple Sensor Expert System for Diagnostic Reasoning, Monitoring and Control of Mechanical Systems,” Mech. Sys. & Sig. Processing, Vol.2, Issue 2, pp. 165-185, 1988.
  3. [3] S. Rangwala and D. Dornfeld, “Sensor Integration Using Neural Networks for Intelligent Tool Condition Monitoring,” Trans ASME, J. Eng. for Ind., Vol.112, No.2, pp. 219-228, 1990.

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Last updated on Nov. 08, 2019