A Finite-Memory Discretization Algorithm for the Distributed Parameter Maxwell-Slip Model

Modeling and compensating for hysteresis are widely adopted to eliminate hysteresis. The distributed parameter Maxwell-slip (DPMS) model is developed from the Maxwell-slip model by replacing the spring-slider elements with an elastic-sliding cell with distributed parameters. Motivated by the mechani...

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Bibliographic Details
Published in:IEEE/ASME transactions on mechatronics 2020-04, Vol.25 (2), p.1138-1142
Main Authors: Liu, Yanfang, Xie, Shaobiao, Du, Desong, Qi, Naiming
Format: Article
Language:English
Subjects:
Actuators
Algorithms
Discretization
Distributed parameter
finite memory (FM)
Frequency modulation
Hysteresis
IEEE transactions
Magnetic hysteresis
Mathematical model
Mathematical models
Mechatronics
modeling and identification
Parameters
piezoelectric actuator (PEA)
Piezoelectric actuators
Piezoelectricity
Predictive models
Slip
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Summary:Modeling and compensating for hysteresis are widely adopted to eliminate hysteresis. The distributed parameter Maxwell-slip (DPMS) model is developed from the Maxwell-slip model by replacing the spring-slider elements with an elastic-sliding cell with distributed parameters. Motivated by the mechanism of human memory, this article proposes a finite-memory (FM) discretization approach for the DPMS model. The change in the infinite internal state is represented by updating the finite peak points. The FM approach is verified using a piezoelectric actuator, and the normalized mean square error is 0.27%. Thus, the FM approach is also advantageous for managing small-amplitude excitations.
ISSN:1083-4435
1941-014X
DOI:10.1109/TMECH.2020.2975264