基于改进SPSO算法优化LS-SVM的六极径向混合磁轴承转子位移自检测技术

Self-sensing Modeling of Rotor Displacement for Six-pole Radial Hybrid Magnetic Bearing Using Improved Simplified Particle Swarm Optimization LS-SVM

为解决磁轴承中采用电涡流传感器或霍尔传感器检测转子位移引起的磁轴承体积大、成本高、可靠性降低等问题,提出一种基于改进的简化粒子群算法优化最小二乘支持向量机位移预测模型的磁轴承转子位移自检测技术。介绍六极径向混合磁轴承的结构和工作原理,并推导其径向悬浮力的数学模型;基于支持向量机回归原理,建立六极径向混合磁轴承的控制线圈电流与转子位移之间的预测模型,并利用改进的简化粒子群算法优化了最小二乘支持向量机的性能参数,实现磁轴承的转子位移自检测。构建六极径向混合磁轴承系统转子位移自检测仿真模型,并进行起浮仿真实验,仿真试验结果证明了该方法的可行性。

In order to solve the problems of large volume,high cost and low reliability caused by eddy current sensors or Hall sensors in magnetic bearings,a self-sensing control method based on the improved simplified particle swarm optimization(SPSO)least square support vector machine(LS-SVM)prediction model was proposed for six-pole radial hybrid magnetic bearing(HMB).The structure and principle of the six-pole radial HMB were introduced,and the mathematical model of its radial suspension force was derived according to the equivalent magnetic circuit method.Based on the principle of the support vector machine,the nonlinear predictive model between the currents and the rotor displacements was established,and the performance parameters of LS-SVM were optimized by using the improved SPSO algorithm,which realized the rotor displacement self-sensing control.The self-sensing simulation model of the six-pole radial HMB system was constructed,and the floating simulation experiment was carried out,which shows the feasibility of the self-sensing method.