本研究构建了一种高速纳米定位平台，该平台采用对称驱动结构，由多层并联粘连的薄层压电陶瓷组成，能够实现微米或纳米尺度的精密操作。针对其固有的迟滞非线性问题，提出了一种基于神经网络的切换输出调节控制器（NN-SORC）以实现非线性补偿。为解决浮点运算速度慢和编译效率低的问题，设计并开发了一种基于现场可编程门阵列–中央处理器（FPGA–CPU）双层数据处理架构的闭环控制系统。通过设计反馈线性化方法，对系统中的迟滞非线性进行线性化处理，从而构建了一个切换跟踪误差系统。在Lyapunov理论和平均驻留时间技术的支持下，推导出了保证NN-SORC闭环系统在实际微/纳米检测与制造过程中常见的切换参考信号作用下实现渐近稳定的充分条件。最后，通过大量对比实验验证了所提出NN-SORC方案的有效性和优越性。

This study establishes a high-speed nano-positioning stage composed of a symmetrically driven structure with multiple parallel-bonded thin piezoelectric ceramic layers capable of performing micro- or nano-scale manipulations. Accordingly, a neural-network-based switching output regulation controller (NN-SORC) was developed to compensate for the associated hysteresis nonlinearity. To address the challenges of slow floating-point computation speeds and low compilation efficiency, a closed-loop control system with a field-programmable gate array-central processing unit (FPGA-CPU) dual-layer data-processing framework was developed. A feedback linearization method was designed to linearize the hysteresis nonlinearity of the framework, resulting in a switching-tracking error system. With the assistance of Lyapunov theory and an average dwell time technique, sufficient conditions were derived to ensure the asymptotic stability of the NN-SORC governing closed-loop system using the switching reference signals often encountered in realistic micro-/nano-scale detection and manufacturing processes. Finally, extensive comparative experiments were conducted to verify the effectiveness and superiority of the proposed NN-SORC scheme.