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

• URL: http://arxiv.org/abs/2604.25691v1
• Date: Tue, 28 Apr 2026 14:20:44 GMT
• Title: Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots
• Abstract summary: Tendon-Driven Robots Continuum (TDCRs) pose significant modeling and control challenges due to complex nonlinearities.<n>This paper proposes a differentiable learning framework that integrates high-fidelity dynamics modeling with robust neural control.
• Score: 23.655392951446995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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