Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, 980-8579, Japan.
Bioinspir Biomim. 2021 Nov 2;16(5). doi: 10.1088/1748-3190/ac1b6f.
Robotic devices with soft actuators have been developed to realize the effective rehabilitation of patients with motor paralysis by enabling soft and safe interaction. However, the control of such robots is challenging, especially owing to the difference in the individual deformability occurring in manual fabrication of soft actuators. Furthermore, soft actuators used in wearable rehabilitation devices involve a large response delay which hinders the application of such devices for at-home rehabilitation. In this paper, a feed-forward control method for soft actuators with a large response delay, comprising a simple feed-forward neural network (FNN) and an iterative learning controller (ILC), is proposed. The proposed method facilitates the effective learning and acquisition of the inverse model (i.e. the model that can generate control input to the soft actuator from a target trajectory) of soft actuators. First, the ILC controls a soft actuator and iteratively learns the actuator deformability. Subsequently, the FNN is trained to obtain the inverse model of the soft actuator. The control results of the ILC are used as training datasets for supervised learning of the FNN to ensure that it can efficiently acquire the inverse model of the soft actuator, including the deformability and the response delay. Experiments with fiber-reinforced soft bending hydraulic actuators are conducted to evaluate the proposed method. The results show that the ILC can learn and compensate for the actuator deformability. Moreover, the iterative learning-based FNN serves to achieve a precise tracking performance on various generalized trajectories. These facts suggest that the proposed method can contribute to the development of robotic rehabilitation devices with soft actuators and the field of soft robotics.
已经开发出带有软执行器的机器人设备，通过实现软、安全的交互，为运动麻痹患者提供有效的康复治疗。然而，这种机器人的控制具有挑战性，特别是由于软执行器手工制造时个体可变性的差异。此外，用于可穿戴康复设备的软执行器涉及较大的响应延迟，这阻碍了此类设备在家庭康复中的应用。在本文中，提出了一种具有大响应延迟的软执行器前馈控制方法，包括一个简单的前馈神经网络（FNN）和一个迭代学习控制器（ILC）。所提出的方法有利于软执行器逆模型（即可以从目标轨迹生成对软执行器的控制输入的模型）的有效学习和获取。首先，ILC 控制软执行器并迭代学习执行器的可变形性。随后，训练 FNN 以获得软执行器的逆模型。ILC 的控制结果用作 FNN 的监督学习的训练数据集，以确保它可以有效地获取软执行器的逆模型，包括可变形性和响应延迟。使用纤维增强软弯曲液压执行器进行实验，以评估所提出的方法。结果表明，ILC 可以学习和补偿执行器的可变形性。此外，基于迭代学习的 FNN 可以在各种广义轨迹上实现精确的跟踪性能。这些事实表明，所提出的方法可以为带有软执行器的机器人康复设备的开发和软机器人领域做出贡献。
