Impedance model force control using a neural network-based effective stiffness estimator

F. Nagata, T. Mizobuchi, T. Hase, Z. Haga, K. Watanabe, Maki K. Habib

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

Abstract

In manufacturing industries of metallic molds, various NC machine tools are used. We have already proposed a desktop NC machine tool with compliance control capability to automatically cope with the finishing process of LED lens molds. The NC machine tool has an ability to control the polishing force acting between an abrasive tool and a workpiece. The force control method is called impedance model force control. The most effective gain is the desired damping of the impedance model. Ideally, the desired damping is calculated from the critical damping condition in consideration of the effective stiffness in force control system. However, there exists a problem that the effective stiffness of the NC machine tool has undesirable nonlinearity. The nonlinearity gives bad influences to the force control stability. In this paper, a fine tuning method of the desired damping is considered by using neural networks. The neural networks acquire the nonlinearity of effective stiffness. The promise is evaluated through an experiment.

Original languageEnglish
Title of host publicationProceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10
Pages697-700
Number of pages4
Publication statusPublished - 2010
Event15th International Symposium on Artificial Life and Robotics, AROB '10 - Beppu, Oita, Japan
Duration: Feb 4 2010Feb 6 2010

Publication series

NameProceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10

Other

Other15th International Symposium on Artificial Life and Robotics, AROB '10
CountryJapan
CityBeppu, Oita
Period2/4/102/6/10

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Human-Computer Interaction

Fingerprint Dive into the research topics of 'Impedance model force control using a neural network-based effective stiffness estimator'. Together they form a unique fingerprint.

  • Cite this

    Nagata, F., Mizobuchi, T., Hase, T., Haga, Z., Watanabe, K., & Habib, M. K. (2010). Impedance model force control using a neural network-based effective stiffness estimator. In Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10 (pp. 697-700). (Proceedings of the 15th International Symposium on Artificial Life and Robotics, AROB 15th'10).