Positioning accuracy of micropositioning stage in scanning ion conductance microscopy is the key to obtain high-precision scanning model. Most piezoelectric ceramic micromotion platforms are used for that, and hysteresis characteristics are the main reason for the nonlinear characteristics of piezoelectric ceramics and the influence on the control accuracy. In order to solve this problem, backpropagation algorithm based on whale optimization algorithm is used to model the hysteresis, which is directly used as a feedforward controller to compensate the hysteresis effect, and the robust adaptive moving average control method is used for feedback control. The results show that the hysteresis model of backpropagation algorithm based on the whale optimization algorithm can fit the hysteresis curve well, and the maximum fitting error is 0.2050 μm, only 0.256%. By adopting feedforward and feedback, feedforward robust adaptive moving average control algorithm decreases the hysteresis from 17.64% to 2.51%, which enables the output of the piezoelectric ceramic controller to track the expected displacement well and makes it possible to improve the scanning accuracy.

中文翻译：

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基于WOA的复合控制方法在SICM微定位阶段的应用

扫描离子电导显微镜中微定位台的定位精度是获得高精度扫描模型的关键。为此，大多数使用压电陶瓷微动平台，而磁滞特性是压电陶瓷非线性特性及其对控制精度的影响的主要原因。为了解决这个问题，采用基于鲸鱼优化算法的反向传播算法对磁滞进行建模，直接将其用作补偿磁滞效应的前馈控制器，并采用鲁棒的自适应移动平均控制方法进行反馈控制。结果表明，基于鲸鱼优化算法的反向传播滞后模型可以很好地拟合滞后曲线，最大拟合误差为0.2050。 μ M，只有0.256％。通过采用前馈和反馈，前馈鲁棒的自适应移动平均控制算法将磁滞从17.64％降低到2.51％，这使压电陶瓷控制器的输出能够很好地跟踪预期的位移，并有可能提高扫描精度。