论文标题
可调磁铁:动态和精确应用的建模和验证
Tunable Magnets: modeling and validation for dynamic and precision applications
论文作者
论文摘要
储层计算是预测湍流的有力工具,其简单的架构具有处理大型系统的计算效率。然而,其实现通常需要完整的状态向量测量和系统非线性知识。我们使用非线性投影函数将系统测量扩展到高维空间,然后将其输入到储层中以获得预测。我们展示了这种储层计算网络在时空混沌系统上的应用,该系统模拟了湍流的若干特征。我们表明,使用径向基函数作为非线性投影器,即使只有部分观测并且不知道控制方程,也能稳健地捕捉复杂的系统非线性。最后,我们表明,当测量稀疏、不完整且带有噪声,甚至控制方程变得不准确时,我们的网络仍然可以产生相当准确的预测,从而为实际湍流系统的无模型预测铺平了道路。
Actuator self-heating limits the achievable force and can cause unwanted structural deformations. This is especially apparent in quasi-static actuation systems that require the actuator to maintain a stable position over an extended period. As a solution, we use the concept of a Tunable Magnet. Tunable magnets rely on in-situ magnetization state tuning of AlNico to create an infinitely adjustable magnetic flux. They consist of an AlNiCo low coercivity permanent magnet together with a magnetizing coil. After tuning, the AlNiCo retains its magnetic field without further energy input, which eliminates the static heat dissipation. To enable implementation in actuation systems, the AlNiCo needs to be robustly tunable in the presence of a varying system air-gap. We achieve this by implementing a magnetization state tuning method, based on a magnetic circuit model of the actuator, measured AlNiCo BH data and air-gap flux feedback control. The proposed tuning method consists of 2 main steps. The prediction step, during which the required magnet operating point is determined, and the demagnetization step, where a feedback controller drives a demagnetization current to approach this operating point. With this method implemented for an AlNiCo 5 tunable magnet in a reluctance actuator configuration, we achieve tuning with a maximum error of 15.86 "mT" and a minimum precision of 0.67 "mT" over an air-gap range of 200 "μm". With this tuning accuracy, actuator heating during static periods is almost eliminated. Only a small bias current is needed to compensate for the tuning error.