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ILC-driven control enhancement for integrated MIMO soft robotic system

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Abstract

This study presents a methodology employing Iterative Learning Control (ILC) to enhance the control performance of soft grippers equipped with multiple curvatures and variable stiffness. ILC is a learning-based control approach that progressively reduces errors in repetitive tasks, known for delivering superior performance in complex systems. In the context of the increasing utilization of robotic technology across various industries, the control technology of soft robots, especially soft grippers with multiple curvatures and variable stiffness, is a crucial issue. While prior research has focused on single-curvature and single-input single-output (SISO) systems, this study addresses the intricate control problem of multi-input multi-output (MIMO) soft gripper systems capable of multiple curvatures. It also proposes an enhanced design for soft grippers with multiple curvatures and variable stiffness while highlighting the potential of ILC for enhancing control performance.

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Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. RS-2023-00209266).

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Authors and Affiliations

  1. Department of Mechanical Engineering, Sungkyunkwan University, Suwon, Gyeongii-do, 16419, Republic of Korea

    Eun Jeong Song, Dong Jun Oh, Ji Min Beak & Ja Choon Koo

  2. Department of Ultra-Precision Machines and Systems, Korea Institute of Machinery and Materials, Gajeongbuk-Ro, Daejeon, 34103, Republic of Korea

    Seung Guk Baek

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  1. Eun Jeong Song
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  2. Seung Guk Baek
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  3. Dong Jun Oh
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  4. Ji Min Beak
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Correspondence to Ja Choon Koo.

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Song, E.J., Baek, S.G., Oh, D.J. et al. ILC-driven control enhancement for integrated MIMO soft robotic system. Intel Serv Robotics 17, 357–368 (2024). https://doi.org/10.1007/s11370-024-00511-y

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  • DOI: https://doi.org/10.1007/s11370-024-00511-y

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