Physics-informed reservoir computing with autonomously switching readouts: a case study in pneumatic artificial muscles
Physics-informed reservoir computing with autonomously switching readouts: a case study in pneumatic artificial muscles
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具有自主切换读数的物理信息储层计算：气动人造肌肉的案例研究
DOI：
10.1109/mhs53471.2021.9767178
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
K. Nakajima
中科院分区：
文献类型：
--
作者：
W. Sun;Nozomi Akashi;Yasuo Kuniyoshi;K. Nakajima
我们介绍了一种基于物理信息的神经网络的方法，从一系列压力测量中预测McKibben气动人工肌肉（PAM）的长度。我们实现了一个回声状态网络，这是一种递归神经网络，具有对应于PAM的不同物理状态的自主切换读数。我们在当前研究中关注的物理状态是受滞后影响的运动方向。开关通过引入门结构来实现，其状态也通过使用输出PAM长度的相同递归网络来控制。我们证明了通过切换读出来处理PAM的不同物理状态将在预测PAM的长度方面稳健地产生性能。我们还证明了高斯混合模型作为一个分类器，用于聚类的储层状态的自主和分类的结果是一致的物理状态的PAM。
We introduce an approach based on physics-informed neural networks to predict the length of a McKibben pneumatic artificial muscle (PAM) from a series of pressure measurements. We implemented an echo state network, which is a type of recurrent neural network with autonomously switching readouts corresponding to the different physical states of the PAM. The physical state we focus on in the current study is the direction of motion affected by hysteresis. The switching is realized by introducing gate architecture, whose states are also controlled by using the same recurrent network that outputs the length of the PAM. We demonstrated that handling the different physical states of the PAM by switching readouts will robustly yield performance in predicting the length of the PAM. We also demonstrated that Gaussian mixture models as a classifier for clustering the reservoir state autonomously and the results in classification are consistent with the physical state of the PAM.