Automatic learning control for unbalance compensation in active magnetic bearings
Automatic learning control for unbalance compensation in active magnetic bearings
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DOI：
10.1109/tmag.2005.851866
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发表时间：
2005
影响因子：
2.1
通讯作者：
Chao Bi;Dezheng Wu;Quan Jiang;Zhejie Liu
中科院分区：
文献类型：
--
作者：
Chao Bi;Dezheng Wu;Quan Jiang;Zhejie Liu
本文提出了一种新的控制方案，自动学习控制，以消除不平衡的影响，积极磁轴承的工作。这种控制方法是基于时域迭代学习控制和增益调度控制。该控制器可以利用最优控制电流进行不平衡补偿。此外，在学习过程中采用了可变学习周期和可变学习增益，以获得更好的抗转速波动的性能。该控制算法不需要大的存储空间和密集的计算。对控制系统进行了实验测试，实验结果表明，该控制方法在较宽的运行速度范围内是有效的。
This paper proposes a new control scheme, automatic learning control, to eliminate unbalance effects, which adversely affect the operation of active magnetic bearings. This control method is based on time-domain iterative learning control and gain-scheduled control. The controller can utilize the optimal control currents for the unbalance compensations. In addition, the variable learning cycle and variable learning gain are employed in the learning process to achieve better performance against rotating speed fluctuations. The control algorithm does not require large memory size and intensive computation. We tested the control system in experiments, and the experimental results prove that the control method is effective over a wide range of operation speeds.