@inbook{b6716e63e26c4161807435aab0da08f7,
title = "PINN-Based Dynamics Learning of Continuum Soft Robots with Finite-Time Stable Sliding Mode Controller",
abstract = "Aircrafts working with soft robotic arms aroused extensive concerns in recent years. In this paper, a physics-informed neural network (PINN) is used to learn the dynamics of continuum soft robots. In detail, we incorporate Euler Lagrange equation into the deep neural network, providing improved physical interpretability and learning efficiency. Afterwards, a finite-time stable sliding mode controller is designed to achieve fast convergence, more accurate trajectory tracking and anti-disturbance ability. Theoretical analysis proves the closed-loop stability, and comparative simulations verify its superiority in transient and steady-state performance.",
keywords = "DeLaN, Finite-time Convergence, PINN, Sliding Mode Control",
author = "Linke Xu and Sen Guo and Dong Zhu and Xiangyu Shao",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.",
year = "2025",
doi = "10.1007/978-981-96-3240-4\_27",
language = "英语",
series = "Springer Aerospace Technology",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "252--259",
booktitle = "Springer Aerospace Technology",
address = "德国",
}