RBF neural network dynamic sliding mode control based on lambert W function for piezoelectric stick–slip actuator

控制理论(社会学) 滑模控制 稳健性(进化) 执行机构 李雅普诺夫函数 控制器(灌溉) PID控制器 控制系统 人工神经网络 工程类 计算机科学 控制工程 控制(管理) 温度控制 人工智能 非线性系统 物理 量子力学 生物化学 化学 电气工程 生物 农学 基因
作者
Yan Li,Piao Fan,Zhenguo Zhang,Yuelong Li,Shitong Yang,Xiaohui Lu
出处
期刊:Review of Scientific Instruments [American Institute of Physics]
卷期号:95 (6)
标识
DOI:10.1063/5.0199060
摘要

This paper presents a novel approach for increasing the precision of high-precision positioning control experiments for a piezoelectric stick–slip actuator system. This is achieved through dynamic sliding mode control with a radial basis function neural network (RBFNN) based on the Lambert W function. The proposed control strategy is divided into two parts: scanning mode control and stepping mode control. For scanning control, a dynamic sliding mode controller was designed to solve the jitter problem in traditional sliding mode control. The introduction of the RBFNN avoids the effects of uncertainty terms and unknown disturbances in the model; reduces the controller gain, which must be adjusted; and improves the robustness of the system to disturbances. The stability of the dynamic sliding mode controller based on the RBFNN was verified through a Lyapunov analysis, and the Lambert W function was introduced to optimize the controller parameters responsible for the time lag in the closed-loop control system. This optimization improved the system’s robustness against time delays, which can adversely affect its performance. Simulation and experimental results indicated that the proposed control strategy achieved a positioning control accuracy of <40 nm during the scanning phase and was robust in the presence of a load. In long-distance positioning control experiments, the control strategy achieved a control target of 40 μm while maintaining the positioning control accuracy and reducing the impact of time lag on the system.

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