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首页> 外文期刊>Journal of low frequency noise, vibration and active control >Off-road Vehicle Seat Suspension Optimisation, Part II: Comparative Study between Meta-Heuristic Optimisation Algorithms
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Off-road Vehicle Seat Suspension Optimisation, Part II: Comparative Study between Meta-Heuristic Optimisation Algorithms

机译:越野汽车座椅悬架优化,第二部分:元启发式优化算法之间的比较研究

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摘要

Clinical observations show that a significant number of low back pain, spine disorders and head injuries are related to exposure to vibration of vehicles. Implementation of seat to suspensions reduces the effects of vibration in vehicle operators in long terms. This paper discusses three evolutionary algorithms in off-road seat suspension design. Genetic algorithm (GA), particle swarm optimisation (PSO) and harmony search (HS) algorithm were used to minimise transmitted vibration to the driver's spine which was modelled by artificial neural network biodynamic model based on experimental data, as was discussed in part I. The reliability of results was studied by simulation of vibration in vertical direction. The suspension was tuned based on harmony search depicting lower values of seat to spine vibration transmissibility and mobility. Thus, the transmitted vibration to spine and risk of spine problem may be alleviated by using this tuned seat suspension.
机译:临床观察表明,大量的下背部疼痛,脊柱疾病和头部受伤与车辆振动有关。座椅到悬架的实现可长期降低车辆驾驶员的振动影响。本文讨论了越野座椅悬架设计中的三种进化算法。遗传算法(GA),粒子群优化(PSO)和和声搜索(HS)算法用于最小化传递给驾驶员脊柱的振动,该振动是基于实验数据由人工神经网络生物动力学模型建模的,如第一部分所述。通过模拟垂直方向的振动来研究结果的可靠性。悬架是根据和谐搜索进行调整的,该搜索描述了座椅到脊柱的振动传递性和活动性较低的值。因此,通过使用这种调节的座椅悬架,可以减轻传递到脊柱的振动和脊柱问题的风险。

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