基于粒子群优化算法的磁浮列车自抗扰悬浮控制器研究

Research on active disturbance rejection suspension controller of maglev train based on particle swarm optimization

为提高磁浮列车悬浮系统的抗干扰性和稳定性,本文提出基于粒子群优化算法(PSO)的自抗扰悬浮控制器。首先建立单电磁铁悬浮系统模型,然后基于该模型设计自抗扰控制器,最后引入粒子群优化算法对控制参数进行自适应寻优,得到适用于系统模型的自抗扰控制器。仿真和单悬浮架试验平台试验结果表明,系统受垂向加速度和悬浮气隙干扰时,相较于传统比例积分微分(PID)控制器,经粒子群优化算法自适应寻优的自抗扰控制器具有更好的抗干扰性和鲁棒性,可为磁浮列车悬浮系统控制算法的工程应用提供新思路。

In order to improve the anti-interference and stability of maglev train suspension system,an active disturbance rejection suspension controller based on particle swarm optimization(PSO)is proposed.Firstly,the suspension system model of single electromagnet is established,and the active disturbance rejection controller is designed based on the model.Finally,the particle swarm optimization is introduced to self-adapt the control parameters,and the active disturbance rejection controller suitable for the system model is obtained.The simulation and single suspension platform test results show that compared with the traditional proportional integral differential(PID)controller,the PSO adaptive auto-disturbance rejection controller has better anti-interference and robustness when the system is disturbed by vertical acceleration and suspension air gap,which provides a new idea for the engineering application of maglev train suspension control algorithm.