机器学习评估PI控制对桥梁虚拟码头系统中中性平衡的影响
Wen-Pei Sung1, Ming-Hsiang Shih2
1Department of Landscape Architecture, National Chin-Yi University of Technology, Taichung, 41170, Taiwan. wps@ncut.edu.tw.
Scientific reports
|October 21, 2025
概括
这项研究使用中性平衡机制 (NEM) 和机器学习优化了桥梁位移控制. 最佳参数显著减少了垂直移位和稳定时间,证实了智能控制系统的有效性.
科学领域:
- 结构工程 结构工程
- 控制系统工程 控制系统工程
- 机器学习应用 机器学习应用
背景情况:
- 主动控制系统对于桥梁在动态负载下的稳定性至关重要.
- 中性平衡机制 (NEM) 提供了一种新的方法来减轻结构振动.
- 优化比例积分 (PI) 控制器收益对于有效的振动控制至关重要.
研究的目的:
- 调查NEM在主动桥梁控制系统中的应用.
- 分析比例增益 (GP) 和积分增益 (GI) 对垂直位移稳定的影响.
- 开发和验证用于预测和优化控制参数的机器学习模型.
主要方法:
- 在一个闭环系统中使用了一个规模化的桥梁模型,具有双NEM,传感器和伺服电机.
- 使用随机森林回归和神经网络用于非线性预测建模.
- 应用K-means集群和特征灵敏度分析以优化参数.
主要成果:
- 最佳PI参数 (GP=1.0,GI=0.010) 将最大垂直位移从~5mm降至~0.4mm.
- 在最佳配置下,稳定时间减少到9.8秒.
- 神经网络模型实现了高预测精度 (R2=0.934,RMSE=0.038).
结论:
- 基于机器学习的模型是可行的桥梁位移控制.
- 数据驱动的参数优化为智能桥梁设计提供了有效的指导.
- 中等增益设置 (GP=1.0,GI=0.010) 最好平衡稳定性和结构对称性.
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