4.6 Article

A novel model of magnetorheological damper with hysteresis division

期刊

SMART MATERIALS AND STRUCTURES
卷 26, 期 10, 页码 -

出版社

IOP Publishing Ltd
DOI: 10.1088/1361-665X/aa87d6

关键词

magnetorheological; damping model; hysteresis division; multiplication type add-rule

资金

  1. National Natural Science Foundation of People's Republic of China [51675063, 51275539]
  2. Chongqing University Postgraduates' Innovation Project [CYB15017]
  3. Program for New Century Excellent Talents in University [NCET-48913-0630]

向作者/读者索取更多资源

Due to the complex nonlinearity of magnetorheological (MR) behavior, the modeling of MR dampers is a challenge. A simple and effective model of MR damper remains a work in progress. A novel model of MR damper is proposed with force-velocity hysteresis division method in this study. A typical hysteresis loop of MR damper can be simply divided into two novel curves with the division idea. One is the backbone curve and the other is the branch curve. The exponential-family functions which capturing the characteristics of the two curves can simplify the model and improve the identification efficiency. To illustrate and validate the novel phenomenological model with hysteresis division idea, a dual-end MR damper is designed and tested. Based on the experimental data, the characteristics of the novel curves are investigated. To simplify the parameters identification and obtain the reversibility, the maximum force part, the non-dimensional backbone part and the non-dimensional branch part are derived from the two curves. The maximum force part and the non-dimensional part are in multiplication type add-rule. The maximum force part is dependent on the current and maximum velocity. The non- dominated sorting genetic algorithm II (NSGA II) based on the design of experiments (DOE) is employed to identify the parameters of the normalized shape functions. Comparative analysis is conducted based on the identification results. The analysis shows that the novel model with few identification parameters has higher accuracy and better predictive ability.

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