基于神经网络的自适应式高度跟踪控制,用于主动空气悬挂系统的自适应式高度跟踪控制,该系统具有磁铁修复学流体阻尼器,受不确定质量和输入延迟的影响
Rongchen Zhao1, Haifeng Xie1, Xinle Gong2
1School of Mechanical and Electrical Engineering, Guizhou Normal University, Guiyang 550001, China.
Sensors (Basel, Switzerland)
|January 11, 2024
概括
这项研究引入了一个强大的自适应神经网络控制磁流体阻尼器活性空气悬挂系统,确保精确的行车高度跟踪,尽管不确定性和延迟.
科学领域:
- 控制系统工程 控制系统工程
- 汽车工程 汽车工程
- 人工智能的人工智能
背景情况:
- 活动式空气悬挂系统 (AAS) 对于车辆的舒适性和操控性至关重要.
- 磁石流体阻尼器 (MRD) 提供可调节的阻尼,但呈现复杂的动态.
- 弹质量的不确定性和时间变化的输入延迟挑战了精确的行驶高度控制.
研究的目的:
- 为MRD-AAS开发一个强大的自适应神经网络控制框架.
- 为了解决弹质量和时间变化的输入延迟的不确定性.
- 为了实现准确可靠的骑行高度跟踪.
主要方法:
- 利用辐射基函数神经网络 (RBFNN) 来近似未建模的MRD动态.
- 开发了一个基于投影仪的估计器,用于不确定的弹质量变化.
- 在自适应控制法中集成了一个时间延迟补偿器.
- 在稳定性分析中使用了Lyapunov-Krasovskii (LK) 函数.
主要成果:
- 拟议的框架确保了行车高度跟踪和估计器错误的稳健融合.
- 实现了系统状态的统一终极边界性.
- 模拟结果表明,在随机道路刺激下,驾驶高度的调节是准确和可靠的.
- 使用AMESim和Matlab/Simulink共同模拟验证的有效性.
结论:
- 新型强大的自适应神经网络控制框架有效地管理MRD-AAS系统.
- 该系统证明了对参数不确定性和输入延迟的弹性.
- 拟议的方法为汽车应用中的行车高度控制提供了可靠的解决方案.
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