Piezoelectric actuators (PEAs) are the pivotal components of many nanopositioning systems because of their superiorities in bandwidth, mechanical force, and precision. Unfortunately, the intrinsic nonlinear property, hysteresis, makes it difficult to achieve the precise control of PEAs. Considering this drawback, diversified feedback control approaches have been studied in the literature. Inspired by the idea that the involvement of feedforward terms can upgrade the tracking performance, our previous conference paper proposed a novel feedforward–feedback control approach (model predictive control with hysteresis compensation). Following the previous work, an adaptive fuzzy predictive controller with hysteresis compensation is further studied in this paper. The major improvement of the proposed method is the employment of adaptive fuzzy model, by which the dynamic model of PEAs is able to adjust in real time, resulting in a better control performance. To validate the effectiveness of the proposed method, extensive experiments are conducted on a Physik Instrumente P-753.1CD piezoelectric nanopositioning stage. Comparisons with several existing control approaches are carried out, and the root mean square tracking error of the proposed method is reduced to 30% of that under the previously proposed neural network model–based predictive control, when tracking 100 Hz sinusoidal reference.

中文翻译：

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压电执行器的具有磁滞补偿的自适应模糊预测控制器

压电执行器（PEA）由于其在带宽，机械力和精度方面的优越性，是许多纳米定位系统的关键部件。不幸的是，固有的非线性特性（磁滞）使其难以实现PEA的精确控制。考虑到这一缺点，文献中已经研究了多种反馈控制方法。受前馈项的参与可以提高跟踪性能的想法启发，我们之前的会议论文提出了一种新颖的前馈-反馈控制方法（具有磁滞补偿的模型预测控制）。在此基础上，本文进一步研究了具有滞后补偿的自适应模糊预测控制器。该方法的主要改进是采用了自适应模糊模型，PEA的动态模型能够实时调整，从而获得更好的控制性能。为了验证所提出方法的有效性，在Physik Instrumente P-753.1CD压电纳米定位平台上进行了广泛的实验。与几种现有控制方法进行了比较，当跟踪100 Hz正弦参考时，该方法的均方根跟踪误差降低到先前基于神经网络模型的预测控制下的均方根跟踪误差的30％。