Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866912805250138112 |
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| author | Bettini, Leonardo Kazemipour, Amirhossein Katzschmann, Robert K. Haller, George |
| author_facet | Bettini, Leonardo Kazemipour, Amirhossein Katzschmann, Robert K. Haller, George |
| contents | Artificial muscles are essential for compliant musculoskeletal robotics but complicate control due to nonlinear multiphysics dynamics. Hydraulically amplified electrostatic (HASEL) actuators, a class of soft artificial muscles, offer high performance but exhibit memory effects and hysteresis. Here we present a data-driven reduction and control strategy grounded in spectral submanifold (SSM) theory. In the adiabatic regime, where inputs vary slowly relative to intrinsic transients, trajectories rapidly converge to a low-dimensional slow manifold. We learn an explicit input-to-output map on this manifold from forced-response trajectories alone, avoiding decay experiments that can trigger hysteresis. We deploy the SSM-based model for real-time control of an antagonistic HASEL-clutch joint. This approach yields a substantial reduction in tracking error compared to feedback-only and feedforward-only baselines under identical settings. This record-and-control workflow enables rapid characterization and high-performance control of soft muscles and muscle-driven joints without detailed physics-based modeling. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03247 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data Bettini, Leonardo Kazemipour, Amirhossein Katzschmann, Robert K. Haller, George Dynamical Systems Computational Engineering, Finance, and Science Robotics Systems and Control Optimization and Control Artificial muscles are essential for compliant musculoskeletal robotics but complicate control due to nonlinear multiphysics dynamics. Hydraulically amplified electrostatic (HASEL) actuators, a class of soft artificial muscles, offer high performance but exhibit memory effects and hysteresis. Here we present a data-driven reduction and control strategy grounded in spectral submanifold (SSM) theory. In the adiabatic regime, where inputs vary slowly relative to intrinsic transients, trajectories rapidly converge to a low-dimensional slow manifold. We learn an explicit input-to-output map on this manifold from forced-response trajectories alone, avoiding decay experiments that can trigger hysteresis. We deploy the SSM-based model for real-time control of an antagonistic HASEL-clutch joint. This approach yields a substantial reduction in tracking error compared to feedback-only and feedforward-only baselines under identical settings. This record-and-control workflow enables rapid characterization and high-performance control of soft muscles and muscle-driven joints without detailed physics-based modeling. |
| title | Nonlinear Spectral Modeling and Control of Soft-Robotic Muscles from Data |
| topic | Dynamical Systems Computational Engineering, Finance, and Science Robotics Systems and Control Optimization and Control |
| url | https://arxiv.org/abs/2601.03247 |