In this brief, a decomposition-learning-based output tracking approach is proposed to compensate for both hysteresis and dynamics effects on output tracking of hysteresis systems such as smart actuators. Simultaneous hysteresis and dynamics control (SHDC) is needed to fully exploit smart or soft actuators/sensors for high-speed, large-range positioning/tracking. It remains still, however, as a challenge to achieve SHDC with both precision (performance) and robustness in general output tracking (i.e., not restricted to periodic/repeated operations), and without complicity in modeling and controller design and/or online implementation. The proposed approach aims to address these challenges, by utilizing libraries of input–output elements constructed offline to online decompose the partially known (i.e., previewed) desired output trajectory and synthesize the control input. Iterative learning control techniques are used a priori to obtain the input elements each tracking the corresponding output elements accurately, and the Preisach modeling of hysteresis is employed to obtain the combination coefficients of the synthesized control input. An experimental implementation to high-speed, large-range nanopositioning using piezoelectric actuator is presented to demonstrate the efficiency and efficacy of the proposed approach in achieving SHDC.

中文翻译：

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基于分解学习的输出跟踪同步滞后和动态控制：高速大范围纳米定位示例

在本简报中，提出了一种基于分解学习的输出跟踪方法，以补偿滞后和动态对滞后系统（例如智能执行器）的输出跟踪的影响。需要同步滞后和动态控制 (SHDC) 来充分利用智能或软执行器/传感器进行高速、大范围定位/跟踪。然而，在一般输出跟踪（即，不限于周期性/重复操作）中实现具有精度（性能）和鲁棒性的 SHDC，并且在建模和控制器设计和/或在线实现方面没有复杂性，仍然是一个挑战。所提出的方法旨在通过利用输入输出元素库来解决这些挑战离线构建 至 在线的分解部分已知（即预览）的期望输出轨迹并合成控制输入。使用迭代学习控制技术先验获得每个精确跟踪相应输出元素的输入元素，并采用迟滞的Preisach建模来获得合成控制输入的组合系数。提出了使用压电执行器进行高速、大范围纳米定位的实验实现，以证明所提出的方法在实现 SHDC 方面的效率和功效。