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• W3092547982 endingPage "107697" @default.
• W3092547982 startingPage "107697" @default.
• W3092547982 abstract "This paper has developed a 5-DOF driver and seat suspension system model for active vibration control. A novel fast system parameter identification method from vibration measurement data has been proposed for the seat-occupant system based on the multi-objective Genetic Algorithm optimization (GA). This system parameter identification method can identify the seat system parameters of a 5-DOF lumped mass-spring-dashpot biodynamic seat-occupant model from vibration test results quickly and accurately. Without calculation and measurement of materials, the physical parameters of the seat suspension system such as masses (m), stiffness (k), and damping coefficients (c) are estimated through matching the measured resonant frequency and transmissibility amplitude at a specific frequency with the simulated ones. This is one of the main contributions of this paper. The characteristics of the human body vibration in the low-frequency range are analyzed through the seat to head transmissibility (STHT) ratio. The experimental and simulation results of the STHT values have been calculated and compared to verify each other. The sensitivity analysis of the seat effective amplitude transmissibility (SEAT) values over the seat system parameters have been conducted and validated by the measured results of the transmissibility ratios. A full state feedback controller has been developed to reduce the human body vibration in the seat suspension system, which is another new contribution of this paper. The simulation results show that the proposed controller has better vibration attenuation performance than the conventional PID controller." @default.
• W3092547982 created "2020-10-15" @default.
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• W3092547982 date "2021-02-01" @default.
• W3092547982 modified "2023-10-16" @default.
• W3092547982 title "Parameter identification and robust vibration control of a truck driver’s seat system using multi-objective optimization and genetic algorithm" @default.
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• W3092547982 doi "https://doi.org/10.1016/j.apacoust.2020.107697" @default.
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