Journal
IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
Volume 67, Issue 7, Pages 5595-5603Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TIE.2019.2934022
Keywords
Hysteresis; Silicon; Inverse problems; Adaptation models; Integrated circuit modeling; Strain; Computational modeling; Generalized Maxwell-slip (GMS) model; hysteresis; online identification and compensation; piezoelectric actuator (PEA)
Categories
Funding
- National Natural Science Foundation of China [51705109, U1737207, 11672093]
- Shanghai Academy of Spaceflight Technology [SAST-2018-109]
Ask authors/readers for more resources
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%.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available