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Study on Fuzzy Control of MR into Semi - Active Suspension
Technical Paper
2018-01-0561
ISSN: 0148-7191, e-ISSN: 2688-3627
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English
Abstract
Suspension has a great influence on vehicle ride comfort and handling stability. How to improve the suspension performance has received more and more attention. To improve vehicle ride comfort, the magnetorheological damper (MRD) semi-active suspension is studied in this paper. Firstly, the dynamic calibration experiment of MRD was carried out so that the mechanical property curves was obtained. According to the experimental results, the Bouc-Wen model of MRD was identified and validated by Simulink Design Optimization. Secondly, The 1/4 of the vehicle vibration model can construct and calculate the vibration differential equations. The suspension of the simulation model can be constructed by the use of Matlab/Simulink software. Based on the established model, we can do an in-depth research on the active suspension control strategies under different road conditions and make related control strategies use the transfer function method. Then, taking the strong nonlinear of MRD itself into account, the fuzzy control algorithm is used to design the semi-active controller, which is realized by the single-chip microcomputer. Finally, in the Simulink, the magneto-rheological semi-active suspension is simulated and analyzed. We do the research on simulation of active suspension fuzzy control and get the simulation results for different road excitation and speeds. Simulation and experimental results show that the fuzzy control of the semi-active suspension can effectively improve the overall performance of the vehicle suspension. Compared with the passive suspension, the overall performance of the MRD semi-active suspension under the random pavement excitation is obviously improved. It provides the theoretical basis and numerical reference for the experimental study of the semi-active suspension.
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Long, H., "Study on Fuzzy Control of MR into Semi - Active Suspension," SAE Technical Paper 2018-01-0561, 2018, https://doi.org/10.4271/2018-01-0561.Data Sets - Support Documents
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