基于人工智能负载估计系统的磁浮列车垂向振动 主动控制

Active Control of Vertical Vibration for Maglev Train Based on Artificial Intelligence Load Estimation System

摘要:

提出了基于人工智能负载估计系统的磁浮列车悬浮系统主动控制方法。给出单点悬浮数学模型，并基于劳斯?赫尔维兹判据证明该模型开环不稳定；考虑负载特征和实时悬浮变化，利用多层人工神经网络对悬浮系统控制量的输出进行主动控制；采用非支配排序遗传算法（NSGA）对系统参数进行优化。结果表明：所提出的控制方法具有较好的鲁棒性，在较大负载扰动时仍然能够保持相对较小的误差。

Abstract:

An active control strategy of maglev train suspension system based on artificial intelligence load estimation system is proposed in this paper. Firstly, the mathematical model of single-point levitation is given, and the open-loop instability is proven by the Routh-Herwitz criterion. Secondly, considering the load characteristics and the real-time suspension changes, a multi-layer artificial neural network is constructed to control the output of the control variables for the suspension system. Thirdly, the non-dominated sorting genetic algorithm (NSGA) is used to optimize the system parameters. The results show that the proposed control method has better robustness and can still keep relatively small error under large load disturbance.