Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866915964333850624 |
|---|---|
| author | Zou, Ziqing Qiu, Ke Wang, Fei Lu, Haojian Xiong, Rong Wang, Yue |
| author_facet | Zou, Ziqing Qiu, Ke Wang, Fei Lu, Haojian Xiong, Rong Wang, Yue |
| contents | Tendon-Driven Continuum Robots (TDCRs) pose significant modeling and control challenges due to complex nonlinearities, such as frictional hysteresis and transmission compliance. This paper proposes a differentiable learning framework that integrates high-fidelity dynamics modeling with robust neural control. We develop a GRU-based dynamics model featuring bidirectional multi-channel connectivity and residual prediction to effectively suppress compounding errors during long-horizon auto-regressive prediction. By treating this model as a gradient bridge, an end-to-end neural control policy is optimized through backpropagation, allowing it to implicitly internalize compensation for intricate nonlinearities. Experimental validation on a physical three-section TDCR demonstrates that our framework achieves accurate tracking and superior robustness against unseen payloads, outperforming Jacobian-based methods by eliminating self-excited oscillations. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_25691 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots Zou, Ziqing Qiu, Ke Wang, Fei Lu, Haojian Xiong, Rong Wang, Yue Robotics Tendon-Driven Continuum Robots (TDCRs) pose significant modeling and control challenges due to complex nonlinearities, such as frictional hysteresis and transmission compliance. This paper proposes a differentiable learning framework that integrates high-fidelity dynamics modeling with robust neural control. We develop a GRU-based dynamics model featuring bidirectional multi-channel connectivity and residual prediction to effectively suppress compounding errors during long-horizon auto-regressive prediction. By treating this model as a gradient bridge, an end-to-end neural control policy is optimized through backpropagation, allowing it to implicitly internalize compensation for intricate nonlinearities. Experimental validation on a physical three-section TDCR demonstrates that our framework achieves accurate tracking and superior robustness against unseen payloads, outperforming Jacobian-based methods by eliminating self-excited oscillations. |
| title | Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots |
| topic | Robotics |
| url | https://arxiv.org/abs/2604.25691 |