基于GA-SVM算法永磁同步直线电机变载荷进给位置误差预测

针对永磁同步直线电机精密进给过程中,受到齿槽效应、端部效应及摩擦力扰动等非线性因素的影响,位置误差难以预测问题,提出了一种基于遗传算法(GA)优化支持向量机(SVM)算法永磁直线电机变载荷位置误差预测模型的方法。通过测量各种情况下永磁直线电机在运动过程中的位置变化情况,利用遗传算法优化支持向量机算法建立预测模型。该模型采用实验台运行的正弦轨迹数据为训练样本,三角波轨迹数据为测试样本。选取各种情形的正弦波轨迹数据和三角波轨迹数据进行仿真预测和验证。以各种情况的正弦波信号的指令位置、指令速度和电流作为模型的输入,以三角波信号的位置误差作为输出。结果表明,经过遗传算法优化支持向量机建立的位置误差预测模型,在拟合和预测精度上要优于未经过算法优化的位置误差预测模型。

Prediction of Feeding Position Error of Permanent Magnet Synchronous Linear Motor Based on GA-SVM Algorithm

In the precision feeding process of permanent magnet synchronous linear motor, it is difficult to predict the position error due to nonlinear factors such as cogging, end effect and frictional disturbance. A genetic algorithm (GA) optimization support vector is proposed. Machine (SVM) algorithm for permanent magnet linear motor variable load position error prediction model. By measuring the position change of permanent magnet linear motor in motion under various conditions, the genetic algorithm is used to optimize the support vector machine algorithm to establish the prediction model. The model uses the sinusoidal trajectory data of the experimental bench as the training sample, and the triangular wave trajectory data is the test sample. The sine wave trajectory data and the triangular wave trajectory data of various situations are selected for simulation prediction and verification. The command position, command speed, and current of the sine wave signal in various cases are used as the input of the model, and the position error of the triangular wave signal is used as the output. The results show that the position error prediction model established by genetic algorithm optimization support vector machine is better than the position error prediction model without algorithm optimization in fitting and prediction accuracy.