磁悬浮列车
悬挂(拓扑)
模型预测控制
汽车工程
控制理论(社会学)
控制(管理)
计算机科学
工程类
控制工程
人工智能
数学
电气工程
同伦
纯数学
作者
Meiqi Liu,Hongfu Shi,Han Wu,Xin Liang,Weiwei Zhang,Xiaohui Zeng
标识
DOI:10.1177/10775463241258003
摘要
To maintain the stable suspension of high-speed maglev vehicles, a predictive control algorithm based on neural networks is proposed. Initially, the vehicle dynamic response prediction model is built using the long short-term memory neural network considering its’ time-varying and nonlinear characteristics. This predictive model achieves precise online prediction of the electromagnetic suspension gap. Then, the prediction model is utilized to construct the predictive control algorithm. Finally, the effectiveness of this algorithm is verified by simulations and experiments. The results demonstrate that the prediction model can accurately and continuously predict the maglev vehicle’s future dynamic responses. Predictive control algorithms can predict fluctuations in the suspension gap before they occur and provide feedforward compensation. Experimental results prove that the predictive control algorithm can effectively suppress electromagnet fluctuations to achieve better stable suspension.
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