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| 基于滞后量化器的磁流变半主动悬架控制策略研究 |
| Research on Control Strategy of Magnetorheological Semi-active Suspension Based on Hysteresis Quantizer |
| 投稿时间：2026-02-01 |
| DOI：10.16018/j.cnki.cn32-1650/n.202602008 |
| 中文关键词: 半主动悬架 磁流变阻尼器 神经网络 自适应鲁棒控制 滞后量化器 |
| 英文关键词: semi-active suspension magnetorheological damper neural networks adaptive robust control hysteresis quantizer |
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| 中文摘要: |
| 针对传统汽车悬架系统存在无法同时保证行驶平顺性与操控稳定性等问题,基于径向基函数神经网络与滞后量化器,提出了磁流变半主动悬架控制策略,建立了七自由度整车动力学模型,最后通过仿真实验进行验证。结果表明,与一般的鲁棒控制方法相比,在鲁棒控制器中引入滞后量化器和自适应控制策略,可以有效降低计算强度和衰减系统非线性引起的干扰;使用模糊神经网络设计的MR阻尼器的逆模型,证明实际电流信号和预测电流信号之间具有良好的一致性;在随机道路激励下,使用SRBF-HQ的自适应鲁棒控制的垂直加速度、俯仰角加速度、横滚角加速度的均方根值与峰值均比被动悬架系统与鲁棒滑模控制器的高,显著提升了整车的振动抑制与乘坐舒适性。 |
| 英文摘要: |
| In response to the problems of traditional automotive suspension systems that cannot simultaneously ensure driving smoothness and handling stability, a magnetorheological semi-active suspension control strategy is proposed based on radial basis function neural networks and hysteresis quantizers. A seven-degree-of-freedom vehicle dynamics model is established, and finally verified through simulation experiments. The results show that compared with general robust control methods, introducing hysteresis quantizers and adaptive control strategy in the robust controller can effectively reduce the computational intensity and attenuate the interference caused by system nonlinearity; The inverse model of the MR damper designed using a fuzzy neural network proves good consistency between the actual current signal and the predicted current signal; Under random road excitation, the root mean square and peak values of vertical acceleration, pitch acceleration, and roll acceleration of adaptive robust control using SRBF-HQ are higher than those of passive suspension system and robust sliding mode controller, significantly improving the vibration suppression and ride comfort of the entire vehicle. |
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