The ability to accurately model mechanical hysteretic behavior in
tendon-actuated continuum robots using deep learning approaches is a growing
area of interest. In this paper, we investigate the hysteretic response of two
types of tendon-actuated continuum robots and, ultimately, compare three types
of neural network modeling approaches with both forward and inverse kinematic
mappings: feedforward neural network (FNN), FNN with a history input buffer,
and long short-term memory (LSTM) network. We seek to determine which model
best captures temporal dependent behavior. We find that, depending on the
robot's design, choosing different kinematic inputs can alter whether
hysteresis is exhibited by the system. Furthermore, we present the results of
the model fittings, revealing that, in contrast to the standard FNN, both FNN
with a history input buffer and the LSTM model exhibit the capacity to model
historical dependence with comparable performance in capturing rate-dependent
hysteresis.

探讨了使用深度学习方法准确模拟肌腱致动连续机器人的机械滞后行为的能力，通过比较三种神经网络建模方法的正向和逆向运动映射（前馈神经网络（FNN），带有历史输入缓冲区的 FNN 和长短期记忆（LSTM）网络），研究了两种类型的肌腱致动连续机器人的滞后响应，并确定哪种模型更能准确捕捉时间依赖行为。发现根据机器人的设计，不同的运动学输入可以改变系统是否表现出滞后现象，同时揭示了模型拟合的结果，与标准 FNN 相比，带有历史输入缓冲区的 FNN 和 LSTM 模型表现出类似的性能，可以准确建模历史相关性和速率依赖滞后。