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随着微纳技术的快速发展，旋转式压电纳米探针台在高真空扫描电子显微镜(SEM)中的应用需求日益增加。为了解决压电驱动设备面临的非线性特性和不确定性干扰，提出一种基于径向基函数(RBF)神经网络的自适应滑模控制(RBF-ASMC)方法。通过结合拉格朗日方法和压电材料的迟滞模型，建立了旋转式压电纳米探针台的动力学模型，并采用RBF神经网络逼近系统未知动态，利用神经网络自适应律优化控制参数。仿真结果表明，RBF-ASMC方法在轨迹跟踪精度和收敛时间方面具有显著优势，稳态误差为0.01°,平均响应时间为1.2 s,相比传统反馈线性化控制(FLC)和非线性干扰观测器滑模控制(NDOSMC)分别减少58.3%和44.4%。这些结果证明，RBF-ASMC在提升控制精度的同时，显著提高系统的动态响应和抗干扰能力。
Abstract：With the rapid development of micro-nano technology, the demand for rotational piezoelectric nano-probe stages in high-vacuum scanning electron microscope(SEM) is increasing. To address the nonlinear characteristics and uncertain interference of piezoelectric-driven devices, an adaptive sliding mode control(RBF-ASMC) method based on radial basis function(RBF) neural networks was proposed. Combining the Lagrange method and the hysteresis model of piezoelectric materials, the dynamic model of the rotational piezoelectric nano-probe stage was established. The RBF neural network was used to approximate the unknown dynamics of the system, and the control parameters were optimized by the neural network adaptive laws. Simulation results show the RBF-ASMC method has significant advantages in trajectory tracking accuracy and convergence time, with a steady-state error of 0.01° and an average response time of 1.2 s, reducing by 58.3% and 44.4% compared to traditional feedback linearization control(FLC) and nonlinear disturbance observer sliding mode control(NDOSMC). These results demonstrate that RBF-ASMC improves control accuracy while enhancing dynamic response and anti-interference ability of the system.
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基本信息:
DOI：10.13250/j.cnki.wndz.25050503
中图分类号:TB383.1;TP273;TP13
引用信息:
[1]马诗雨,孟思源,丁戍辰,等.基于RBF自适应滑模的旋转式压电纳米探针台轨迹跟踪控制[J].微纳电子技术,2025,62(05):107-117.DOI:10.13250/j.cnki.wndz.25050503.
基金信息:
国家重点研发计划项目(2023YFF0721400); 国家自然科学基金项目(62273247)
2024-11-22
2024
2024-12-20
2025-01-01
2025
1
2025-05-14
2025-05-14