What does this research mean for the field?

Deep learning methods, specifically long short-term memory (LSTM) and gated recurrent unit (GRU) networks, can be used to model the forward hysteresis behavior of magnetorheological fluid-based brakes. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

The aim is to model the hysteresis input-output relationship of magnetorheological (MR) brakes using deep learning methods.

April 13, 2026Engineering Science and Technology an International JournalOpen Access

Hysteresis modeling of magnetorheological fluid-based (MR) brakes with deep learning

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Authors

SKSefa Furkan KüçükoğluIzmir Institute of Technology YBYalın Baştanlar MDM. İ. Can DedeIzmir Institute of Technology

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Implication

Models the hysteresis behavior of magnetorheological brakes using deep learning techniques, suggesting enhanced system design.

Key Points

The aim is to model the hysteresis input-output relationship of magnetorheological (MR) brakes using deep learning methods.
Develop deep learning models, specifically LSTM and GRU, for hysteresis modeling.
Implement a data pre-processing step to diversify input signals.
Evaluate the models using RMSE and R² metrics on test signals.
The deep learning methods demonstrated effective modeling of MR brake hysteresis behavior.
LSTM and GRU provided similar performance in terms of RMSE and R² values.
Data pre-processing enhanced the models' robustness and predictive capabilities.

Cite This Study

Küçükoğlu et al. (2026) studied this question.

synapsesocial.com/papers/69dc88f43afacbeac03eaa92 https://doi.org/10.1016/j.jestch.2026.102371

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Hysteresis modeling of magnetorheological fluid-based (MR) brakes with deep learning

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Cite This Study

Hysteresis modeling of magnetorheological fluid-based (MR) brakes with deep learning

Discussion

Member takes

Implication

Key Points

Cite This Study

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