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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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Published in: | IEEE/ASME transactions on mechatronics 2020-04, Vol.25 (2), p.1138-1142 |
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creator | Liu, Yanfang Xie, Shaobiao Du, Desong Qi, Naiming |
description | 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. |
doi_str_mv | 10.1109/TMECH.2020.2975264 |
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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 |
title | A Finite-Memory Discretization Algorithm for the Distributed Parameter Maxwell-Slip Model |
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