具有径向基网络加速度反馈的磁浮列车悬浮系统滑模控制

Sliding Mode Control of Maglev Train Suspension System with Neural Network Acceleration Feedback

摘要:

为了保证磁浮列车的悬浮稳定性，研究了悬浮系统的主动控制问题。首先，基于磁浮列车单电磁铁最小悬浮单元建立了对应的电流控制数学模型，并结合仿真说明了比例?积分?微分（PID）控制算法对非线性负载等时变干扰非常敏感的问题；然后，提出了一种采用分岔理论稳定性证明的滑模控制方法，并结合径向基函数（RBF）神经网络的参数自调整功能构建了具有振动抑制的悬浮控制模块，有效地抑制了电磁铁振动；最后，通过构造Simulink控制模型并搭建单电磁铁悬浮试验平台进行仿真和试验。结果表明：电磁铁振动对悬浮性能的影响尤为明显，所提出控制算法能够在复杂扰动存在的情况下对电磁铁振动进行有效抑制，并提高悬浮系统的动态性能。

Abstract:

In order to ensure the suspension stability of maglev train， the active control of suspension system is studied. Firstly， based on the minimum suspension unit of single electromagnet of maglev train， the corresponding control mathematical model of current is established. Combined with the simulation， it is shown that the proportion-integration-differentiation（PID）control algorithm is very sensitive to time-varying disturbances such as nonlinear load. Then， a sliding mode control method based on the stability proof of bifurcation theory is proposed. Combined with the parameter self-adjusting function of radial basis function （RBF） neural network， a suspension control module with vibration suppression is constructed to effectively suppress the vibration of electromagnet. Finally， the Simulink control model is constructed and the single electromagnet suspension experimental platform is built for relevant simulation and experiments. The results show that the effect of electromagnet vibration on the suspension performance is particularly obvious. The proposed control algorithm can effectively suppress the electromagnet vibration in the presence of complex disturbances and improve the dynamic performance of the suspension system.