基于BP神经网络模型的磁流变减振器半主动悬架控制新方案

Honghui Zhang, Zhiyuan Zou, Hang Su
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引用次数: 0

摘要

磁流变阻尼技术在悬架控制领域具有广阔的应用前景,在奢侈品领域已基本实现商业化。然而,由于悬架控制和磁流变阻尼器控制的交叉交叉,使得车辆磁流变半主动控制的发展十分复杂。本文提出了一种基于BP神经网络的驱动控制新方案,将磁流变阻尼器封装成一个黑匣子,通过嵌入式驱动器实现励磁电流和阻尼力之间的强非线性映射。将传感器嵌入到磁流变阻尼器中进行集成解决,并提出了解决磁流变阻尼器沉降问题的机理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A New Scheme for Semi-active Suspension Control based on BP Neural Network Model of Magnetorheological Damper
Magnetorheological (MR) controllable damping is promising in suspension control and almost commercialized in luxuries. However, the development of MR semi-active control for vehicles is complicated because of the messed interdisciplinary process both in the suspension control and the MR damper control. In this paper, a new scheme of driving control based on BP neural network is proposed to package the MR damper as a black box implementing the strong nonlinearity mapping between the excitation current and damping force by the embedded driver. The sensor also embedded in the MR damper for integrated solution, and a mechanism for tackling the sedimentation problem of the MR damper are also pointed out.
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