@inproceedings{40cdf0f0a55d411da0ef9c52c299d826,
title = "Learning-Based Optimal Impedance Control for Space Manipulator Contact Tasks",
abstract = "This paper focuses on the contact control problem during operation tasks executed by space manipulator systems. First, a discounted value function is established to describe the interaction performance between the end-effector and the contact surface of object. Then, taking into account the effect of contact surface position, a model-free integral reinforcement learning (IRL) method with state feedback is applied to solve the optimal impedance parameters. Later, a state reconstruction technique with immunity to control noise is developed, whereby a novel model-free IRL algorithm is proposed which obviates the reliance on the velocity measurement and contact dynamics knowledge. Numerical simulations reveal the effectiveness of the proposed algorithm in space contact tasks. {\textcopyright} 2023 IEEE.",
keywords = "impedance control, integral reinforcement learning, optimal control, space manipulator",
author = "Han Wu and Kaipeng Sun and Qinglei Hu and Yongxia Shi and Jianying Zheng and Jiawen Wang",
year = "2023",
doi = "10.1109/ICCAR57134.2023.10151722",
language = "English",
isbn = "9798350322521",
series = "International Conference on Control, Automation and Robotics, ICCAR",
publisher = "IEEE",
pages = "199--204",
booktitle = "2023 9th International Conference on Control, Automation and Robotics (ICCAR 2023)",
address = "United States",
note = "9th International Conference on Control, Automation and Robotics (ICCAR 2023), 9th IEEE ICCAR ; Conference date: 21-04-2023 Through 23-04-2023",
}