Cheng Liqun, Chen Wanzhong, Tian Liguo, Xie Ying
College of Communication Engineering, Jilin University, Changchun 130012, China.
International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China.
Sensors (Basel). 2023 Jul 8;23(14):6246. doi: 10.3390/s23146246.
Piezoelectric actuators (PEAs) have the benefits of a high-resolution and high-frequency response and are widely applied in the field of micro-/nano-high-precision positioning. However, PEAs undergo nonlinear hysteresis between input voltage and output displacement, owing to the properties of materials. In addition, the input frequency can also influence the hysteresis response of PEAs. Research on tracking the control of PEAs by using various adaptive controllers has been a hot topic. This paper presents a finite-time sliding-mode controller (SMC) based on the disturbance observer (DOB) and a radial basis function (RBF) neural network (NN) (RBF-NN). RBF-NN is used to replace the hysteresis model of the dynamic system, and a novel finite-time adaptive DOB is proposed to estimate the disturbances of the system. By using RBF-NN, it is no longer necessary to establish the hysteresis model. The proposed DOB does not rely on any priori knowledge of disturbances and has a simple structure. All the solutions of closed-loop systems are practical finite-time-stable, and tracking errors can converge to a small neighborhood of zero in a finite time. The proposed control method was compiled in C language in the VC++ environment. A series of comparative experiments were conducted on a platform of a commercial PEA to validate the method. According to the experimental results of the sinusoidal and triangular trajectories under the frequencies of 1, 50, 100, and 200 Hz, the proposed control method is feasible and effective in improving the tracking control accuracy of the PEA platform.
压电致动器（PEA）具有高分辨率和高频响应的优点，在微/纳高精度定位领域得到广泛应用。然而，由于材料特性，PEA在输入电压和输出位移之间会出现非线性滞后现象。此外，输入频率也会影响PEA的滞后响应。利用各种自适应控制器对PEA进行跟踪控制的研究一直是一个热门话题。本文提出了一种基于干扰观测器（DOB）和径向基函数（RBF）神经网络（NN）（RBF-NN）的有限时间滑模控制器（SMC）。RBF-NN用于替代动态系统的滞后模型，并提出了一种新颖的有限时间自适应DOB来估计系统的干扰。通过使用RBF-NN，不再需要建立滞后模型。所提出的DOB不依赖于任何干扰的先验知识，结构简单。闭环系统的所有解都是实际有限时间稳定的，跟踪误差可以在有限时间内收敛到零的一个小邻域内。所提出的控制方法在VC++环境中用C语言进行了编译。在商用PEA平台上进行了一系列对比实验来验证该方法。根据1、50、100和200Hz频率下正弦和三角轨迹的实验结果，所提出的控制方法在提高PEA平台的跟踪控制精度方面是可行且有效的。
