The pneumatic and hydraulic dual actuation of pressure-driven soft actuators (PSAs) is promising because of their potential to develop novel practical soft robots and expand the range of soft robot applications. However, the physical characteristics of air and water are largely different, which makes it challenging to quickly adapt to a selected actuation method and achieve method-independent accurate control performance. Herein, we propose a novel LAtent Representation-based Feedforward Neural Network (LAR-FNN) for dual actuation. The LAR-FNN consists of an autoencoder (AE) and a feedforward neural network (FNN). The AE generates a latent representation of a PSA from a 30-s stairstep response. Subsequently, the FNN provides an individual inverse model of the target PSA and calculates feedforward control input by using the latent representation. The experimental results with PSAs demonstrate that the LAR-FNN can meet the requirements of dual actuation control (i.e., accurate control performance regardless of the actuation method with a short adaptation time) with a single neural network. The results suggest that a LAR-FNN can contribute to soft dual-actuation robot development and the field of soft robotics.

中文翻译：

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用于压力驱动软执行器的气动和液压双重驱动的基于潜在表示的学习控制器。

压力驱动软体执行器（PSA）的气动和液压双重驱动具有开发新型实用软体机器人和扩大软体机器人应用范围的潜力，因此前景广阔。然而，空气和水的物理特性有很大不同，这使得快速适应选定的驱动方法并实现独立于方法的精确控制性能具有挑战性。在此，我们提出了一种用于双驱动的新型基于 LAtent 表示的前馈神经网络（LAR-FNN）。LAR-FNN 由自动编码器（AE）和前馈神经网络（FNN）组成。AE 根据 30 秒的阶梯响应生成 PSA 的潜在表示。随后，FNN 提供目标 PSA 的单独逆模型，并使用潜在表示计算前馈控制输入。PSA的实验结果表明，LAR-FNN可以用单个神经网络满足双驱动控制的要求（即，无论采用何种驱动方法，都能获得精确的控制性能，且适应时间短）。结果表明，LAR-FNN 可以为软体双驱动机器人的开发和软体机器人领域做出贡献。