用神经网络建模并控制绳驱动连续体机器人，提升精度与抗干扰能力。
Learning-Based Dynamics Modeling and Robust Control for Tendon-Driven Continuum Robots
- 用双向多通道GRU模型建模非线性动力学，抑制长期预测误差。
- 端到端神经控制在真实机器人上实现精准跟踪，无自激振荡。
- 适合做复杂柔性机器人控制的科研与工程人员参考。
绳驱动连续体机器人（TDCRs）因摩擦迟滞、传动柔顺等非线性特性，面临建模与控制难题。本文提出一种可微学习框架，将高保真动力学建模与鲁棒神经控制结合。设计基于GRU的动力学模型，具备双向多通道连接和残差预测机制，有效抑制长时序自回归预测中的误差累积。通过将该模型作为梯度桥梁，端到端神经控制策略经反向传播优化，隐式内化对复杂非线性性的补偿能力。在三段式物理TDCR上的实验表明，该框架实现了高精度轨迹跟踪，并对未见过的负载表现出优越鲁棒性，优于基于雅可比的方法，成功消除自激振荡。实现细节与源码见 https://github.com/ZiqingZou/ContinuumControl。
原文摘要 · Abstract (English)
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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