Flexible continuum manipulators are valued for minimally invasive surgery,
offering access to confined spaces through nonlinear paths. However,
cable-driven manipulators face control difficulties due to hysteresis from
cabling effects such as friction, elongation, and coupling. These effects are
difficult to model due to nonlinearity and the difficulties become even more
evident when dealing with long and multi-segmented manipulator. This paper
proposes a data-driven approach based on recurrent neural networks to capture
these nonlinear and previous states-dependent characteristics of cable
actuation. We design customized fiducial markers to collect physical joint
configurations as a dataset. Result on a study comparing the learning
performance of four Deep Neural Network (DNN) models show that the Temporal
Convolution Network (TCN) demonstrates the highest predictive capability.
Leveraging trained TCNs, we build a control algorithm to compensate for
hysteresis. Tracking tests in task space using unseen trajectories show that
the best controller reduces the mean position and orientation error by 61.39%
(from 13.7 mm to 5.29 mm) and 64.04% (from 31.17{\deg} to 11.21{\deg}),
respectively.

基于循环神经网络的数据驱动方法用于控制弹性连续操纵器，以解决由于绳索效应产生的滑脱、伸长和耦合等不可线性问题，并通过定制的标志物收集物理关节配置数据集来实现最佳控制算法，从而降低位置和方向误差。