An adaptive basic I/O gain tuning method based on leveling control input histogram for human-machine systems

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

A method to tune a basic input/output gain (BIOG) according to usage conditions for human-machine systems is proposed for improving work performance. Adapting a BIOG is effective in terms of improving operability and work efficiency, but frequent changes using a gain scheduling strategy degrade the operability by confusing the machine dynamics. The proposed tuning system adjusts a BIOG at long intervals on the basis of comprehensive features in operator and work content obtained from the histogram of control lever input. The target value is set to the normal distribution, meaning that all ranges of the control lever are evenly used in a spring-type lever, as leveling the histogram independent of an operator and work content provides the consistent operational feeling, which leads to comfortable operability. To ensure practicality and effectiveness, a BIOG curve is set to a polygonal line involving a break point and a saturation point that are tuned by equivalent transform of area differences between the obtained histogram and normal distribution curves. Two types of experimental task were performed using a hydraulic arm system. Results showed that the proposed BIOG tuning system improves time efficiency by reducing the area difference while increasing the subjective usability compared with a conventional fixed BIOG system.

Original languageEnglish
Title of host publicationIEEE International Conference on Intelligent Robots and Systems
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1926-1931
Number of pages6
ISBN (Print)9781479969340
DOIs
Publication statusPublished - 2014 Oct 31
Event2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014 - Chicago
Duration: 2014 Sep 142014 Sep 18

Other

Other2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014
CityChicago
Period14/9/1414/9/18

Fingerprint

Man machine systems
Normal distribution
Tuning
Scheduling
Hydraulics

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Software
  • Computer Vision and Pattern Recognition
  • Computer Science Applications

Cite this

Kamezaki, M., Iwata, H., & Sugano, S. (2014). An adaptive basic I/O gain tuning method based on leveling control input histogram for human-machine systems. In IEEE International Conference on Intelligent Robots and Systems (pp. 1926-1931). [6942817] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IROS.2014.6942817

An adaptive basic I/O gain tuning method based on leveling control input histogram for human-machine systems. / Kamezaki, Mitsuhiro; Iwata, Hiroyasu; Sugano, Shigeki.

IEEE International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc., 2014. p. 1926-1931 6942817.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Kamezaki, M, Iwata, H & Sugano, S 2014, An adaptive basic I/O gain tuning method based on leveling control input histogram for human-machine systems. in IEEE International Conference on Intelligent Robots and Systems., 6942817, Institute of Electrical and Electronics Engineers Inc., pp. 1926-1931, 2014 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2014, Chicago, 14/9/14. https://doi.org/10.1109/IROS.2014.6942817
Kamezaki M, Iwata H, Sugano S. An adaptive basic I/O gain tuning method based on leveling control input histogram for human-machine systems. In IEEE International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc. 2014. p. 1926-1931. 6942817 https://doi.org/10.1109/IROS.2014.6942817
Kamezaki, Mitsuhiro ; Iwata, Hiroyasu ; Sugano, Shigeki. / An adaptive basic I/O gain tuning method based on leveling control input histogram for human-machine systems. IEEE International Conference on Intelligent Robots and Systems. Institute of Electrical and Electronics Engineers Inc., 2014. pp. 1926-1931
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