改进型SVM在轴向磁轴承转子位移自检测中的应用

磁轴承采用位移自检测技术能够减少磁轴承体积、降低成本和提高可靠性。提出了一种基于混合核函数最小二乘支持向量机(LSSVM)预测模型的磁轴承自检测技术。介绍了轴向主动磁轴承的工作原理并推导了其悬浮力的数学模型；在混合核函数LSSVM回归原理的基础上,建立了控制线圈电流与转子位移之间的非线性预测模型,并优化了LSSVM参数,实现了无位移传感器控制。构建了轴向主动磁轴承系统自检测仿真模型,针对所提自检测方法进行了仿真研究,仿真结果表明该模型能够准确预测转子轴向位移。进一步的试验结果表明,该方法具有良好的轴向位移自检测性能,实现了轴向主动磁轴承无位移传感器下稳定悬浮运行。

Application of Improved SVM in SelfSensing of Rotor Displacement inAxial Active Magnetic Bearing

The selfsensing magnetic bearing could reduce the cost and the axial size of the magnetic bearing and increase its reliability. A selfsensing control method based on mixedkernel least squares support vector machine (LSSVM) forecasting model was proposed for an axial active magnetic bearing (AAMB). The principle and mathematical model of the active magnetic bearing were introduced; based on the principle of the mixedkernel LSSVM, the nonlinear forecasting model between the current and the displacement which realized the displacement selfsensing control was built through parameter optimization. The control system of the AAMB with selfsensing was constructed. The simulation results showed that the prediction model could accurately detect the rotor axial displacement. Further experimental results also showed that the method had a good ability of axial displacement selfsensing. The AAMB could realize stable suspension operation without displacement sensors.