控制理论(社会学)
磁滞
执行机构
刚度
反向
计算机科学
补偿(心理学)
可逆矩阵
递归最小平方滤波器
蠕动
算法
数学
工程类
自适应滤波器
材料科学
结构工程
物理
人工智能
控制(管理)
心理学
复合材料
纯数学
量子力学
精神分析
几何学
作者
Yanfang Liu,Yan Wang,Xin Chen
出处
期刊:IEEE Transactions on Industrial Electronics
[Institute of Electrical and Electronics Engineers]
日期:2020-07-01
卷期号:67 (7): 5595-5603
被引量:18
标识
DOI:10.1109/tie.2019.2934022
摘要
Hysteresis in piezoelectric actuators (PEAs) causes significant position errors and limits the application of PEAs. Compensating hysteresis via inverse models is one of the most popular approaches to reduce its effects. However, the construction of an accurate model is very challenging since hysteresis is coupled with creep and high-order dynamics, and hysteresis properties are also affected by operating conditions and aging effects. To overcome these challenges, this article proposes an adaptive generalized Maxwell-slip (AGMS) algorithm to identify and compensate for hysteresis online. By evenly distributing the saturation deformations throughout the desired range, a linear relation between the output force and the spring stiffness is generated, which enables a low-computational-complexity algorithm, such as the recursive least-squares algorithm, to update the stiffness online. Since the GMS model is self-invertible, an inverse model can be further analytically constructed in real time and utilized to compensate for the hysteresis. Experimental and comparison studies are carried out. The results show that this approach relaxes the requirement on the precision of the model and is robust to the operating conditions due to the capability of updating model parameters online. The normalized root mean square tracking error is approximately 0.4%.
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