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Global Learning Position Controls for Permanent-Magnet Step Motors

机译:永磁步进电机的全局学习位置控制

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摘要

Permanent-magnet step motors offer several advantages such as high efficiency, high power density, high torque-to-inertia ratio, and excellent durability and serviceability, as well as the absence of external rotor excitation and windings. The nonuniformity in the developed torque due to the nonsinusoidal flux distribution in the airgap is, however, the major obstacle in achieving global high-precision position tracking. When the position reference profile is a periodic signal of known period, such an obstacle may be however overcome by using recent learning control techniques, which require neither high gains in the inner speed/position control loops nor resetting procedures. An experimental comparison of two different recently designed learning position controls (“adaptive” and “iterative”) is, for the first time, carried out with reference to the same low-speed robotic application. Benefits and drawbacks of the two learning approaches are analyzed in detail.
机译:永磁步进电机具有许多优势,例如效率高,功率密度高,转矩惯性比高,耐用性和可维修性好,并且无需外部转子励磁和绕组。然而,由于气隙中存在非正弦波的通量分布,导致产生的扭矩不均匀,这是实现全局高精度位置跟踪的主要障碍。然而,当位置参考曲线是已知周期的周期性信号时,可以通过使用最新的学习控制技术来克服这种障碍,该学习控制技术既不需要内部速度/位置控制环的高增益,也不需要复位过程。首次参照相同的低速机器人应用程序对两个不同的最近设计的学习位置控件(“自适应”和“迭代”)进行了实验比较。详细分析了两种学习方法的优缺点。

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