论文标题
使用高斯流程回归的交换不情愿电动机的最佳换向
Optimal Commutation for Switched Reluctance Motors using Gaussian Process Regression
论文作者
论文摘要
交换不情愿的电动机很有吸引力，因为它们在建筑和维护方面都很便宜。本文的目的是开发一个换向函数，该功能以减少扭矩波纹的方式线性化非线性电动机动力学。为此，提出了凸优化问题，该问题直接惩罚样品之间的扭矩波纹以及功耗，而高斯过程回归用于获得连续的换向函数。所得函数与常规换向函数根本不同，闭环模拟显示误差显着降低。结果为准确控制不情愿电动机提供了适当的换向功能的新观点。
Switched reluctance motors are appealing because they are inexpensive in both construction and maintenance. The aim of this paper is to develop a commutation function that linearizes the nonlinear motor dynamics in such a way that the torque ripple is reduced. To this end, a convex optimization problem is posed that directly penalizes torque ripple in between samples, as well as power consumption, and Gaussian Process regression is used to obtain a continuous commutation function. The resulting function is fundamentally different from conventional commutation functions, and closed-loop simulations show significant reduction of the error. The results offer a new perspective on suitable commutation functions for accurate control of reluctance motors.
