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arXiv · 2604.25691

Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots

Ziqing Zou·Ke Qiu·Fei Wang·Haojian Lu·Rong Xiong·Yue Wang
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Abstract

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. For implementation details and source code, please refer to https://github.com/ZiqingZou/ContinuumControl.

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Ziqing Zou, Ke Qiu, Fei Wang, Haojian Lu, Rong Xiong, Yue Wang. 2026-04-28. Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots. https://arxiv.org/abs/2604.25691

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