Learning Piezoelectric Actuator Dynamics Using a Hybrid Model Based on Maxwell-Slip and Gaussian Processes

Piezoelectric actuators (PEAs) are increasingly applied to the field of ultraprecision positioning; however, unwanted nonlinearities, such as hysteresis, degrade their performance. This article integrates a Gaussian process (GP) with a Maxwell-slip (MS) model to capture nonlinearities in PEAs. The G...

Full description

Saved in:
Bibliographic Details
Published in:IEEE/ASME transactions on mechatronics 2022-04, Vol.27 (2), p.725-732
Main Authors: Liu, Yanfang, Ni, Chenrui, Du, Desong, Qi, Naming
Format: Article
Language:English
Subjects:
Data models
Displacement
Errors
Gaussian process
Gaussian process (GP)
hybrid model
Hysteresis
Integrated circuit modeling
maxwell-slip (MS) model
Mechatronics
Noise measurement
Nonlinearity
Performance degradation
piezoelectric actuator (PEA)
Piezoelectric actuators
Predictive models
Slip
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Piezoelectric actuators (PEAs) are increasingly applied to the field of ultraprecision positioning; however, unwanted nonlinearities, such as hysteresis, degrade their performance. This article integrates a Gaussian process (GP) with a Maxwell-slip (MS) model to capture nonlinearities in PEAs. The GP predicts the output of a PEA or the error of the MS model by using the current and historical applied input voltages, as well as the measured historical output displacements, as input. The measured output is replaced by the output of the MS model in the case that a displacement sensor is unavailable. The experimental results show that by properly selecting system order, the proposed approach achieves a precise result and provides the boundary of the prediction error in terms of variance. If the output displacement is available and the GP predicts the output of a PEA directly, the normalized root-mean-square error (NRMSE) is as low as 0.29%. The NRMSE is reduced further to 0.14% if the dataset used for prediction is updated online.
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2021.3070187