This paper presents two model-free control strategies for the rejection of unknown disturbances in continuum robots. The strategies utilize a neural network-based approximation technique to estimate the uncertain Jacobian matrix using position measurements. The first strategy is designed for periodic disturbances and employs an adaptive model-free controller in conjunction with an adaptive disturbance observer. The second strategy is designed for robustness against arbitrary disturbances and employs time-varying input and update law gains that grow monotonically, resulting in the achievement of asymptotic, exponential, and prescribed-time reference trajectory tracking. The notion of fixed-time stabilization in prescribed time is particularly noteworthy, as it allows for the predefinition of a terminal time, independent of initial conditions and system parameters. A formal stability analysis is presented for each strategy, and the strategies are both tested experimentally with a concentric tube robot subject to unknown disturbances.

中文翻译：

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连续机器人的自适应无模型干扰抑制


本文提出了两种无模型的控制策略，用于抑制连续体机器人中的未知干扰。这些策略利用基于神经网络的近似技术，通过位置测量来估计不确定的雅可比矩阵。第一种策略专为周期性干扰而设计，并结合使用自适应无模型控制器和自适应干扰观测器。第二种策略旨在实现对任意干扰的鲁棒性，并采用单调增长的时变输入和更新定律增益，从而实现渐近、指数和规定时间参考轨迹跟踪。在规定时间内固定时间的概念特别值得注意，因为它允许独立于初始条件和系统参数预定义终端时间。为每种策略提出了正式的稳定性分析，并且这些策略都使用同心管机器人对未知干扰进行了实验测试。