基于遗传算法的磁浮列车悬浮控制参数优化

悬浮系统本质上是不稳定的，要通过设计控制器使得闭环系统稳定。对单铁悬浮控制模型，采用基于磁通反馈的间隙PID控制算法，通过合理选取控制参数可以达到良好的控制效果。为使悬浮控制器具有良好的跟踪特性同时保证磁浮列车的乘坐舒适性，需要限定悬浮系统闭环带宽。应用遗传算法进行控制参数优化设计，对基于ITAE的适应度函数进行改进，对不满足闭环系统带宽要求的个体进行惩罚，设计基于遗传算法的悬浮控制控制参数优化算法，并通过实例计算验证了算法的有效性。

Parameter Optimization of Maglev Train's Suspension System Based on Genetic Algorithm

Suspension system is unstable in essence, it needs to design a controller to make close - loop system stable. For the single maglev suspension model based on flux feedback and clearance PID, it can achieve excellent performance by choosing proper control parameter. Considering the maglev train following the intended variation in the track position and providing a suitable ride quality, the bandwidth must be restricted. Genetic algorithm is adopted to optimize the parameter. At the same time, fitness function based on ITAE must be redesigned and the individuals which can not meet the requirement of bandwidth restriction must be punished. Finally, the optimized parameter is calculated and the optimization algorithm is valid for suspension system.