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.

翻译：绳驱连续体机器人（TDCRs）因存在摩擦滞后、传动柔顺等复杂非线性特性，给建模与控制带来显著挑战。本文提出一种可微分学习框架，将高保真动力学建模与鲁棒神经控制相结合。我们开发了基于GRU的动力学模型，该模型具有双向多通道连接与残差预测机制，可有效抑制长时间自回归预测中的误差累积。通过将该模型视为梯度桥梁，利用反向传播优化端到端神经控制策略，使其能够隐式内化对复杂非线性的补偿能力。在物理三节段TDCR上的实验验证表明，所提框架在跟踪精度与对抗未知负载的鲁棒性方面均优于基于雅可比的方法，且自发振荡得到消除。